Development and Validation of the Melanoma Cancer Awareness Measure

Liz Wilson; Oliver Nix; Monika Janda; H. Peter Soyer; Samuel G. Smith; Sophie M. C. Green

doi:10.3310/nihropenres.14348.1

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Research Article

Development and Validation of the Melanoma Cancer Awareness Measure

[version 1; peer review: awaiting peer review]

Liz Wilson¹, Oliver Nix¹, Monika Janda², H. Peter Soyer³, Samuel G. Smith¹^*, Sophie M. C. Green¹^*

Liz Wilson¹, Oliver Nix¹, [...] Monika Janda², H. Peter Soyer³, Samuel G. Smith¹^*, Sophie M. C. Green¹^*

^* Equal contributors

PUBLISHED 08 Aug 2026

Author details Author details

¹ University of Leeds Leeds Institute of Health Sciences, Leeds, England, UK
² Princess Alexandra Hospital Centres for Health Research, The University of Queensland, Woolloongabba, Queensland, Australia
³ Dermatology Research Centre, The University of Queensland, Frazer Institute, Woolloongabba, Queensland, Australia

Liz Wilson
Roles: Data Curation, Formal Analysis, Investigation, Project Administration, Visualization, Writing – Original Draft Preparation

Oliver Nix
Roles: Data Curation, Formal Analysis, Investigation, Writing – Original Draft Preparation

Monika Janda
Roles: Conceptualization, Methodology, Writing – Review & Editing

H. Peter Soyer
Roles: Conceptualization, Methodology, Writing – Review & Editing

Samuel G. Smith
Roles: Conceptualization, Methodology, Project Administration, Supervision, Validation, Writing – Review & Editing

Sophie M. C. Green
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Supervision, Validation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS AWAITING PEER REVIEW

Abstract

Background

In the UK, the time taken to recognise a melanoma symptom and seek medical attention is the third longest of all cancers. There is no validated measure to assess public awareness of melanoma. We aimed to develop and validate a reliable Melanoma Cancer Awareness Measure (M-CAM).

Methods

Items for symptoms and risk factors were sourced from national cancer guidelines and patient information websites and reviewed by an expert panel. Three validation studies were undertaken. To assess internal and test-retest reliability, the general public (n = 32) completed the M-CAM on two occasions (~14 days apart). Construct validity was assessed by comparing expert (n = 35) and the general public (n = 100) responses. Sensitivity to change was established by comparing responses of participants randomly allocated to a control leaflet (n = 32) or a melanoma leaflet (n = 29).

Results

Internal reliability was high (Cronbach’s alpha>0.8) for all scales. Public responses were consistent over time, demonstrating test-retest reliability. Experts achieved higher scores than the general public on prompted [11.26(2.70) vs 9.10(3.25),p < 0.001] and unprompted symptom awareness items [3.06(1.81) vs 1.67(1.68),p < 0.001], and unprompted risk factor items [1.26(1.22) vs 0.81(0.95),p = 0.053]. However, prompted risk factor items scores were not different between the groups [10.69(6.07) vs 10.74(4.62),p = 0.422]. The melanoma leaflet group performed better than the control group in the prompted symptom [12.59(1.84) vs 9.75(3.57), p < 0.001], unprompted symptom [4.55(2.08) vs 1.84(1.44),p < 0.001], prompted risk factor [15.59(3.49) vs 12.06(4.40),p < 0.001] and unprompted risk factor items [2.14(1.73) vs 0.87(1.04),p = 0.007] demonstrating the measure is sensitive to change. A risk factor item related to age did not demonstrate construct validity or sensitivity to change.

Conclusions

The M-CAM is a reliable and valid tool to assess melanoma symptom awareness. Further validation may be needed to assess construct validity of the risk factor subscale. Assessing awareness of melanoma in national surveys could identify gaps in knowledge and support early detection and awareness efforts.

Plain Language Summary

Melanoma is a type of skin cancer. Many people do not seek help quickly from a healthcare professional if they notice a symptom, which can lead to worse outcomes. Currently there is no reliable, tested way of measuring how much the public know about the symptoms and risk factors of melanoma. In this study, we aimed to develop a questionnaire called the Melanoma Cancer Awareness Measure (M-CAM).

This involved three studies to assess whether the M-CAM:

1.
Produces similar results over time;
2.
Can differentiate between groups with different levels of melanoma knowledge;
3.
Can detect increases in awareness after education about melanoma.

To do this, we ran three studies:

1.
32 members of the public completed the M-CAM twice, two weeks apart. Results were compared across the two time points.
2.
35 medical doctors completed the M-CAM once. Results were compared with responses from 100 members of the public.
3.
61 members of the public completed the M-CAM twice. Before completing the M-CAM the second time, they were randomly shown a melanoma information leaflet (29 participants) or an unrelated information leaflet (32 participants). Results were compared across the two groups.

All sections of the M-CAM were consistent, meaning the public gave similar answers at two time points. Doctors were aware of more melanoma symptoms, and could list more risk factors than the public. However, when doctors and the public were given a list of risk factors, there was no significant difference between how many they correctly identified. Participants who were shown the melanoma leaflet showed greater improvements in awareness than those shown the unrelated information leaflet.

The M-CAM is a reliable tool for assessing melanoma awareness, but may need further testing. The measure can be used in national surveys to identify gaps in public knowledge.

Keywords

Melanoma, cancer awareness measure, symptoms, risk factors, skin cancer

Corresponding author: Samuel G. Smith

Competing interests: HPS is a shareholder of MoleMap NZ Limited and e-derm consult GmbH and undertakes regular teledermatological reporting for both companies. HPS is a Medical Consultant for Canfield Scientific Inc and a Medical Advisor for First Derm. SGS has received financial compensation from Lilly for attending an expert advisory group. The author(s) declare(s) that there is no conflict of interest regarding the publication of this article.

Grant information: This project is funded by the National Institute for Health and Care Research (NIHR) by an Advanced Fellowship (Grant reference number: NIHR300588) and a Development and Skills Enhancement Award (Grant reference number: NIHR305132). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Professor Smith also acknowledges funding from a Yorkshire Cancer Research University Academic Fellowship (L389SS).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2026 Wilson L et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Wilson L, Nix O, Janda M et al. Development and Validation of the Melanoma Cancer Awareness Measure [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:115 (https://doi.org/10.3310/nihropenres.14348.1) First published: 08 Aug 2026, 6:115 (https://doi.org/10.3310/nihropenres.14348.1) Latest published: 08 Aug 2026, 6:115 (https://doi.org/10.3310/nihropenres.14348.1)

Introduction

Melanoma is the most serious form of skin cancer, with incidence rising rapidly worldwide.¹^–³ It is the fifth most common cancer in the UK, with around 17,500 new diagnoses and 2,300 deaths annually.⁴^,⁵ Populations exposed to high levels of ultraviolet (UV) radiation and those with fair skin and hair are particularly at risk of developing the disease.⁶ The greatest burden of melanoma falls on New Zealand, Australia, North America and Europe.⁷ This has, in part, been attributed to populations in these regions being predominantly fair skinned and having a high lifetime exposure to UV radiation though environmental factors and tanning culture.⁸^–¹⁰ Symptoms of melanoma include newly forming or changing lesions, which are often irregular in shape and colouring, large in size, rapidly growing, and may be bleeding, itchy, crusty or painful.¹¹ Two-week referral to specialist dermatological care is recommended upon recognition of symptoms in the UK.

Low awareness of melanoma symptoms may increase time taken to seek medical support.¹² A UK study of 2371 patients with 15 cancers found that 28.9% of patients with malignant melanoma had a delay (>3 months) between noticing symptoms and first presentation to a doctor, which was the third longest delay of all cancer sites.¹³ One of the most common barriers to presentation of melanoma symptoms to a doctor is not realising a symptom is serious.¹⁴ Increased time prior to first presentation could delay specialist referral, diagnosis, and management, increasing the stage at diagnosis and the likelihood of poorer clinical outcomes.¹⁴^–¹⁷ Later stage melanoma at diagnosis is associated with an increased chance of recurrence and mortality, lower quality of life and increased healthcare costs¹⁵^,¹⁸^–²²

Higher awareness of risk factors of melanoma and preventive measures may help to reduce incidence and improve clinical outcomes. Campaigns implemented in recent decades have aimed to inform the public of behaviours which increase risk of developing melanoma and implement positive attitudes towards preventing exposure to UV radiation.²³^–²⁵ Evidence has suggested that raising awareness could significantly reduce morbidity and mortality, and may be attributable to the rise in proportion of thin 0.1 mm melanomas and fall in thicker melanomas at diagnosis.²³^,²⁶ However, there is a lack of recent data outlining public awareness of symptoms and risk factors associated with melanoma.

The limited research on melanoma awareness may be in part due to the lack of an appropriate validated measure. Such cancer awareness measures (CAMs) have been validated in an established process for other disease sites, including breast, bowel, lung, ovarian and cervical.²⁷^–³¹ These measures capture symptom and risk factor awareness and barriers to seeking medical help. The site-specific CAMs can be used in population and targeted studies to assess awareness of specific cancers, to identify gaps in knowledge, guide focus for interventions and evaluate efficacy of public awareness campaigns.²⁸^,³¹

Using an established process for the development of other site-specific CAMs,³² this series of linked studies aimed to develop and validate a Melanoma Cancer Awareness Measure (M-CAM). Specifically, our objectives were to: 1) test the extent to which the M-CAM shows internal and test-retest reliability; 2) assess whether the M-CAM can distinguish between two distinct groups known to differ in levels of cancer awareness (the ‘known groups’ method); and 3) evaluate whether the M-CAM was sensitive to change.

Methods

Patient and public involvement

Patients and members of the public were not involved in the development of the research question or the design of this study.

The development and validation of the M-CAM followed a process used to validate the general³² and site-specific CAMs.²⁷^–³¹

Phase 1- generating questionnaire items

The M-CAM includes unprompted and prompted questions to assess knowledge of both symptoms and risk factors. Participants are asked the unprompted items prior to prompted items to prevent inflating accurate responses to the unprompted items. The CAMs also include questions to assess confidence detecting symptoms and anticipated behaviours around seeking medical attention as a means of capturing anticipated delays in medical help-seeking.

Melanoma symptoms and risk factors were identified from the National Institute for Health and Clinical Excellence (NICE) ‘Suspected cancer: referral and recognition’ guidelines³³; the NHS ‘Symptoms- melanoma skin cancer’ website³⁴ and the Cancer Council Australia melanoma guidelines.³⁵ Accuracy of items and determination of the correct responses were reviewed and discussed by the study team, including behavioural scientists, medical students and a dermatologist (SS, SG, ON, LW, MJ, PS). Items were removed, added or re-worded accordingly.

A panel of GPs (n = 8) reviewed the M-CAM items via an online survey, to check for interpretability, accuracy, and overlap between items. Overlapping items were removed where necessary and minor changes were made to item wording. The draft version of the M-CAM comprised 41 items; 15 items on symptom awareness (one unprompted and 14 prompted), one item on anticipated delay in seeking medical attention for potential symptoms, 21 items on risk factor awareness (one unprompted and 20 prompted), one item on age associated with the risk of melanoma, two items on previous skin checks and one item on confidence detecting melanoma symptoms. Unprompted items asked participants to list all symptoms or risk factors of melanoma they were aware of, while prompted items presented a list of symptoms or risk factors and asked respondents to indicate whether they could be symptoms or risk factors of melanoma. The full version of the M-CAM can be found as extended data.³⁶ The M-CAM follows scoring for the other site-specific CAMs.²⁷^–³⁰

M-CAM sub-scales and scoring

Symptom awareness. For the unprompted item, one point was given for each symptom accurately identified, verified by referring to the symptoms in the prompted list. Responses were scored by three authors (ON, LW, SG), with any disagreements arbitrated by a fourth author (SS). For prompted items, participants responded with ‘Yes’, ‘No’ or ‘Don’t know’. All ‘yes’ responses were scored as correct (1 point), and all ‘No’ and ‘Don’t know’ responses were scored as incorrect (0 points). A separate score was calculated for unprompted and prompted awareness of symptoms.

Risk factors. The same process for scoring symptom awareness was followed for the risk factors. Any risk factor named in the unprompted recall item that was accurate and present in the closed list of risk factors scored 1 point. For the closed items, participants responded on a 5-point Likert scale (strongly disagree to strongly agree). ‘Strongly agree’ or ‘Agree’ responses were considered accurate (1 point), and ‘Not sure’, ‘Disagree’ and ‘Strongly disagree’ responses were considered incorrect (0 points). A separate score was calculated for unprompted and prompted awareness of risk factors.

Anticipated delay in help-seeking. Participants were asked how long they would anticipate waiting if they had a sign or symptom of melanoma. Response options for anticipated help-seeking ranged from ‘1–3 days’ to ‘more than 3 months’, with additional options of ‘I would not contact my doctor’ and ‘I would contact another health professional’. Items were scored from 1 to 9.

Age and risk. Participants were asked which age group was most likely to develop melanoma or if melanoma was unrelated to age. As risk of melanoma increases with age, responses of ’70 year old’ scored ‘1’, and all other responses were scored ‘0’.

Previous skin checks. Participants were asked if they have had a previous skin check conducted by themselves, a non-clinician or a healthcare professional. ‘Yes’ responses scored ‘1’ and other options scored ‘0’.

Confidence detecting melanoma symptoms. Participants were asked how confident they felt to notice a symptom of melanoma. This item was scored between ‘1’ to ‘4’ (‘not at all confident’- ‘very confident’).

Phase 2- validating the M-CAM

Only symptom awareness, risk factor awareness and age and risk items generated in phase 1 were included in the validation studies. This is because the items related to delays in seeking medical attention and confidence in detecting symptoms had been previously validated in the general CAM³² and did not pertain to awareness or knowledge of melanoma, but rather a behavioural response to symptom identification.²⁸^,³⁷ Ethical approval was granted by the University of Leeds Research Ethics Committee (MREC 22–066), and informed consent was obtained from all participants. All surveys were conducted on Jisc Online Surveys, and data were analysed using SPSS version 28.0.

Study 1: reliability

Aim: To assess the internal and test-retest reliability of the M-CAM items.

Participants: A total of 100 adults from the general UK population were recruited via personal and professional social media accounts of the core study team (LW, ON, SS, SG) and our associated UK-based research groups. The link to the online survey was included in all posts. People were eligible if they were UK adults without clinical or academic medical qualifications.

Procedure: After reading the study information and consenting to participate, participants completed an online survey including demographic information and the M-CAM items. To assess test-retest reliability, half of the sample were asked to complete the M-CAM again two weeks after initial completion. A unique participant ID linked baseline and follow-up responses.

Analysis: Cronbach’s alpha was used to assess internal reliability on the baseline data. A paired group t-test assessed test-retest reliability in participants who completed the M-CAM at two timepoints, for the symptom awareness and risk factor subscales. McNemar’s test was used to assess test-retest reliability for the age risk factor item. The M-CAM was considered reliable if there was no significant difference between baseline and follow-up responses.

Study 2: validity

Aim: To assess the degree to which items measure the construct of melanoma awareness using the ‘known groups’ method.³¹

Participants: Medical doctors (n = 35), considered experts, were recruited via personal and professional relationships of the core study team. Invitation emails, including a link to the online survey were sent to potential participants.

Procedure: Using a cross-sectional design, experts were asked to complete an online survey, consisting of demographic information and the M-CAM. No personally identifiable information was collected.

Analysis: We compared the expert responses with Study 1 baseline general population participant responses (n = 100). As the dependent variables were not normally distributed, we tested the difference in unprompted and prompted recall for symptom awareness and risk factors using Mann-Whitney U tests. Pearson’s chi-square was used to assess difference in response between experts and the general public on the age and risk factor item. The M-CAM was considered valid if the scores between the experts and general population samples were significantly different in the expected direction.

Study 3: sensitivity to change

Aim: To assess whether the M-CAM was sensitive to an expected improvement in knowledge of symptoms and risk factors.

Participants: Data from the UK general population sample used in Study 1 (n = 100) were used in Study 3.

Procedure: After completing the initial survey in Study 1, all participants were randomly allocated (1:1) into two groups using Microsoft Excel. The control group (n = 50) received an information leaflet about influenza that was unrelated to melanoma. The intervention group (n = 50) received an information leaflet that detailed the symptoms and risk factors associated with melanoma. The leaflets were sent via email two weeks after completion of the baseline M-CAM. Participants were instructed to read the leaflets, and then complete the M-CAM for a second time. A unique participant ID linked baseline and follow-up responses.

Analysis: Analysis of covariance (ANCOVA) was used to assess differences between the control and intervention group on unprompted and prompted symptom and risk factor awareness, adjusting for participants’ baseline M-CAM scores. Logistic regression was used for the age as a risk factor item, to determine whether receiving the melanoma leaflet increased participants ability to correctly identify that older age increases risk of melanoma, adjusting for baseline scores on this item.

Results

Participants (n = 100) from the general public completed the M-CAM at time point 1, and 61 (61%) completed the M-CAM two weeks later (32/50 [64%]: control group; 29/50 [58%]: intervention group). Participant data that could not be linked via individual codes was excluded (n = 3). Most respondents at time point 1 were female (76%, n = 76), White (68%, n = 68) and educated to degree level (69%, n = 69) (Table 1). A minority (10%, n = 10) reported a family history of melanoma. Of the 35 experts who responded, 14% (n = 5) had clinical/academic expertise in dermatology or oncology. Most experts (66%, n = 23) had more than 25 years of post-graduate medical experience.

Study 1: reliability

Using responses from all participants at time point one (n = 135), the M-CAM demonstrated good internal reliability for symptoms (α = 0.81) and risk factors (α = 0.88). Internal reliability was also demonstrated for symptoms (α = 0.85) and risk factors (α = 0.85) at time point two (n = 61).

Data from participants who were randomised to the control condition in Study 3, were used to assess test-retest reliability over the two time points. There were no significant differences between time point 1 and 2 scores on unprompted and prompted symptom awareness and risk factor scales, indicating test-retest reliability (Table 2). For the age and risk item, there was no significant difference in participants responses at time point 1 (28/32 incorrect, 4/32 correct) and time point 2 (28/32 incorrect, 4/32 correct) (p = 1.000). This indicates test-retest reliability for the age and risk item.

Study 2: validity

Baseline responses from the general population sample (time point 1, n = 100) and experts (n = 35) were compared to assess construct validity. Experts recognised significantly more melanoma symptoms in the unprompted item (p < 0.001) and prompted scale (p < 0.001), demonstrating construct validity (Table 3). The unprompted risk factor scores were higher for the expert group compared with the general population group, but the difference did not reach statistical significance (p = 0.053). There was no significant difference between the two groups for the prompted risk factor scale (p = 0.422). For the age risk factor item, 28.6% of experts and 15.0% of the general public answered correctly, which was not significantly different (p = 0.075). Construct validity was therefore partially demonstrated for the M-CAM.

Study 3: sensitivity to change

After adjusting for baseline scores, the group that received the melanoma leaflet scored significantly higher than the control group across all symptom and risk factor scales in prompted and unprompted items, demonstrating sensitivity to change ( Table 4). For the age and risk item, 7.8% of participants who received the influenza leaflet correctly identified age as a risk factor, compared with 20.4% of those who received the melanoma leaflet (OR = 5.29, 95% CI 0.99, 28.26, p = 0.052).

Table 1. Descriptive analysis of all participants.

Demographics		General population (N = 100), n(%)	Expert (N = 35), n(%)	Control (N = 32), n(%)	Experimental (N = 29), n(%)
Age
	<30	22 (22.0)	2 (5.7)	5 (15.6)	8 (27.6)
	30–40	19 (19.0)	1 (2.9)	7 (21.9)	1 (3.4)
	41–50	19 (19.0)	5 (14.3)	6 (18.8)	3 (10.3)
	51–60	24 (24.0)	24 (68.6)	7 (21.9)	8 (27.6)
	>60	16 (16.0)	3 (8.6)	7 (21.9)	9 (31.0)
Sex
	Male	24 (24.0)	18 (51.4)	7 (21.9)	6 (20.7)
	Female	76 (76.0)	17 (48.6)	25 (78.1)	23 (79.3)
Ethnicity
	White/Caucasian	68 (68.0)	5 (14.3)	24 (75.0)	23 (79.3)
	Asian	25 (25.0)	30 (85.7)	7 (21.9)	6 (20.7)
	Mixed	4 (4.0)	0 (0.0)	1 (3.1)	0 (0.0)
	Black, Caribbean or African	1 (1.0)	0 (0.0)	0 (0.0)	0 (0.0)
	Other	1 (1.0)	0 (0.0)	0 (0.0)	0 (0.0)
	Prefer not to say	1 (1.0)	0 (0.0)	0 (0.0)	0 (0.0)
Education level
	Degree level	69 (69.0)	35 (100)	20 (62.5)	16 (55.2)
	A-level	9 (9.0)	0 (0.0)	4 (12.5)	3 (10.3)
	GCSE/equivalent	8 (8.0)	0 (0.0)	3 (9.4)	5 (17.2)
	Vocational qualifications	1 (1.0)	0 (0.0)	0 (0.0)	0 (0.0)
	Higher education	9 (9.0)	0 (0.0)	4 (12.5)	4 (13.8)
	Other	3 (3.0)	0 (0.0)	1 (3.1)	0 (0.0)
	No formal qualifications	1 (1.0)	0 (0.0)	0 (0.0)	1 (3.4)
Family history of melanoma
	Yes	10 (10.0)	0 (0.0)	6 (18.8)	2 (6.9)
	No	83 (83.0)	0 (0.0)	24 (75.0)	24 (82.8)
	Don’t know	7 (7.0)	0 (0.0)	2 (6.3)	3 (10.3)

Table 2. Test-retest reliability.

Variable	T1 (N = 32), mean (SD)	T2 (N = 32), mean (SD)	t	df	P value
Symptoms – unprompted	1.56 (1.80)	1.84 (1.44)	−0.99	31	0.331
Symptoms – prompted	9.28 (3.08)	9.75 (3.57)	−0.74	31	0.463
Risk factors – unprompted	0.69 (0.82)	0.88 (1.04)	−0.95	31	0.351
Risk factors – prompted	11.63 (4.00)	12.06 (4.40)	−0.93	31	0.362

Table 3. Construct validity.

	General public (n = 100), mean (SD)	Expert (n = 35), mean (SD)	Mann-Whitney U	P value
Symptoms- unprompted	1.67 (1.68)	3.06 (1.81)	1006.5	<0.001
Symptoms- prompted	9.10 (3.25)	11.26 (2.70)	1084.5	<0.001
Risk factors- unprompted	0.81 (0.95)	1.26 (1.22)	1389.5	0.053
Risk factors- prompted	10.74 (4.62)	10.69 (6.07)	1590.5	0.422

Table 4. Sensitivity to change.

	Control (N = 32), mean (SD)	Experimental (n = 29), mean (SD)	F	df	P value
Symptoms- unprompted	1.84 (1.44)	4.55 (2.08)	34.36	1	<0.001
Symptoms- prompted	9.75 (3.57)	12.59 (1.84)	14.41	1	<0.001
Risk factors- unprompted	0.87 (1.04)	2.14 (1.73)	7.86	1	0.007
Risk factors- prompted	12.06 (4.40)	15.59 (3.49)	22.81	1	<0.001

Discussion

In this series of linked validation studies, we demonstrated that the Melanoma Cancer Awareness Measure (M-CAM) is a valid and reliable tool to assess melanoma awareness. Checks for internal reliability, test-retest reliability and sensitivity to change were all met for the key symptom awareness measure and risk factor scales. Further investigation of construct validity may be needed for the risk factor items, as the general population and expert groups both scored equally poorly on this scale. This could be included as part of future national surveys evaluating the prevalence of melanoma awareness at a population-level.

The marginal difference between the general population and expert samples regarding the risk factor subscales was not expected. Similarities between the general population and expert samples were particularly apparent in the prompted risk factor items and could be explained by a number of factors. In the development studies of other site-specific CAMs, experts performed better than the general public in the symptom and risk factor items.²⁷^–³¹ The expert samples in those studies only included specialists in the specific cancer associated with the respective measure. In comparison, most (86%) experts in our sample reported they did not have any clinical or academic expertise in oncology or dermatology. Resident doctors and GPs also report a lack of confidence in recognising signs and symptoms of melanoma.³⁸^–⁴² Greater than expected scores in the general public participants may be due to the high level of education observed. Future work could therefore verify the validity of the M-CAM by repeating the known groups study with dermatologists.

The M-CAM is available for use in national surveys to assess awareness of symptoms and risk factors of melanoma. Results of such surveys could identify specific gaps in knowledge to inform interventions. Use of the M-CAM would advance upon existing research which has typically used unvalidated surveys.⁴³^–⁴⁹ National surveys could also highlight whether there are particular socio-demographic groups who lack awareness, which has been suggested in previous research.⁵⁰^–⁵³ The results of future M-CAM surveys could be used to identify gaps in knowledge that can be targeted in future melanoma prevention programmes.

Strengths of this work include a diverse sample, with participants from a range of age and ethnic groups. However, these studies had limitations. The majority of the general population sample were female (76%) and had degree level education (69%), which may limit the generalisability of the findings. Recruitment through the research team’s social media networks may account for the high education level of participants. Of the experts, the majority did not have expertise in dermatology or oncology which may have affected their knowledge of melanoma symptoms and risk factors. However, most medical schools incorporate ‘skin cancer’ learning outcomes into their syllabuses globally.⁵⁴^–⁵⁶ Therefore, we would expect clinicians to have more knowledge on melanoma compared to the general public.

The survey was completed by participants unsupervised on an online platform and therefore participants could have searched for the correct answers to the items. While unprompted items were separated onto a different page from prompted items, they were able to return to previous pages and could have altered their free text answers. Attrition between the two time points may have created bias in follow-up. Participants who completed the survey at both points may have had increased interest or knowledge in the topic, although the distribution of age, sex, ethnicity and education levels were broadly similar across the timepoints. We anticipated needing small sample sizes for most studies, however some effect sizes may have been smaller than expected, thereby reducing our statistical power to detect such effects.

In conclusion, after following an established procedure for validating site-specific cancer awareness measures, the M-CAM was shown to be a reliable and valid tool of melanoma awareness. Further verification of the validity of the measure could be undertaken in larger population samples. Such population-based surveys could be used to identify gaps in knowledge and could inform potential interventions and public health campaigns to support the early diagnosis of melanoma.

Consent

Written informed consent was obtained electronically from all participants. All procedures, including the informed consent process, were conducted in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000.

Declaration

We confirm that a conference abstract has been published for a poster presentation. The manuscript has not been published and is not under consideration in any other journals.

Data availability

Underlying data

The dataset is available in an open access data repository. Ethical approval was granted to share the dataset open access in the University of Leeds data repository. All participants consented to their data to be stored in this way.

Repository name: Research Data Leeds Repository.⁵⁷

Data can be accessed at: https://doi.org/10.5518/1865.

Wilson L, Nix O, Janda M, Soyer HP, Smith SG, Green SMC. Development and validation of the Melanoma Cancer Awareness Measure- dataset. University of Leeds. [Dataset]; 2026. https://doi.org/10.5518/1865.

The dataset contains the cleaned dataset in an SPSS file.

Data are available under the Creative Commons Attribution 4.0 International (CC BY 4.0).

Extended data

Open Science Framework: Extended data for ‘Development and Validation of the Melanoma Cancer Awareness Measure’, https://doi.org/10.17605/OSF.IO/HZJ5U.³⁶

This project contains the following files:

- MCAM V1.0.pdf

Data are available under the Creative Commons Attribution licence (CC-BY) 4.0.

Acknowledgements

We would like to express our gratitude to the panel of general practitioners for their contribution towards the development of the M-CAM items. We are also grateful to all survey participants for their valuable time and input.

References

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3. Li Z, Fang Y, Chen H, et al.: Spatiotemporal trends of the global burden of melanoma in 204 countries and territories from 1990 to 2019: Results from the 2019 global burden of disease study. Neoplasia. 2022; 24(1): 12–21. PubMed Abstract | Publisher Full Text | Free Full Text
4. Cancer Research UK: Melanoma skin cancer statistics2015 [05/10/2022]. Reference Source
5. Matthews NH, Li WQ, Qureshi AA, et al.: Epidemiology of Melanoma.Ward WH, Farma JM, editors. Cutaneous Melanoma: Etiology and Therapy. Brisbane (AU): Codon Publications The Authors; 2017. Publisher Full Text
6. Conforti C, Zalaudek I: Epidemiology and Risk Factors of Melanoma: A Review. Dermatol. Pract. Concept. 2021; 11(Suppl 1): e2021161S. PubMed Abstract | Publisher Full Text
7. Karimkhani C, Green AC, Nijsten T, et al.: The global burden of melanoma: results from the Global Burden of Disease Study 2015. Br. J. Dermatol. 2017; 177(1): 134–140. Publisher Full Text
8. Sinclair C, Foley P: Skin cancer prevention in Australia. Br. J. Dermatol. 2009; 161(Suppl 3): 116–123. Publisher Full Text
9. Diffey B: Climate change, ozone depletion and the impact on ultraviolet exposure of human skin. Phys. Med. Biol. 2004; 49(1): R1–R11. PubMed Abstract | Publisher Full Text
10. Boniol M, Autier P, Boyle P, et al.: Cutaneous melanoma attributable to sunbed use: systematic review and meta-analysis. BMJ [Br. Med. J.]. 2012; 345: e4757. PubMed Abstract | Publisher Full Text | Free Full Text
11. Negin BP, Riedel E, Oliveria SA, et al.: Symptoms and signs of primary melanoma: important indicators of Breslow depth. Cancer. 2003; 98(2): 344–348. Publisher Full Text
12. Walter F, Webster A, Scott S, et al.: The Andersen Model of Total Patient Delay: a systematic review of its application in cancer diagnosis. J. Health Serv. Res. Policy. 2012; 17(2): 110–118. PubMed Abstract | Publisher Full Text | Free Full Text
13. Forbes LJ, Warburton F, Richards MA, et al.: Risk factors for delay in symptomatic presentation: a survey of cancer patients. Br. J. Cancer. 2014; 111(3): 581–588. PubMed Abstract | Publisher Full Text | Free Full Text
14. O’Shea SJ, Rogers Z, Warburton F, et al.: Which symptoms are linked to a delayed presentation among melanoma patients? A retrospective study. BMC Cancer. 2017; 17(1): 5. PubMed Abstract | Publisher Full Text | Free Full Text
15. Helvind NM, Brinch-Møller Weitemeyer M, Chakera AH, et al.: Stage-Specific Risk of Recurrence and Death From Melanoma in Denmark, 2008-2021: A National Observational Cohort Study of 25 720 Patients With Stage IA to IV Melanoma. JAMA Dermatol. 2023; 159(11): 1213–1222. PubMed Abstract | Publisher Full Text | Free Full Text
16. Garbe C, Keim U, Amaral T, et al.: Prognosis of Patients With Primary Melanoma Stage I and II According to American Joint Committee on Cancer Version 8 Validated in Two Independent Cohorts: Implications for Adjuvant Treatment. J. Clin. Oncol. 2022; 40(32): 3741–3749. PubMed Abstract | Publisher Full Text | Free Full Text
17. Bedikian AY, Johnson MM, Warneke CL, et al.: Prognostic Factors That Determine the Long-Term Survival of Patients with Unresectable Metastatic Melanoma. Cancer Investig. 2008; 26(6): 624–633. Publisher Full Text
18. Wilson MA, Zhong J, Rosenbaum BE, et al.: Impact of initial stage on metastatic melanoma survival. Melanoma Res. 2019; 29(3): 281–288. PubMed Abstract | Publisher Full Text | Free Full Text
19. Reitmajer M, Leiter U, Nanz L, et al.: Long-term survival of stage IV melanoma patients: evaluation on 640 melanoma patients entering stage IV between 2014 and 2017. J. Cancer Res. Clin. Oncol. 2024; 150(1): 15. PubMed Abstract | Publisher Full Text | Free Full Text
20. Tromme I, Devleesschauwer B, Beutels P, et al.: Health-related quality of life in patients with melanoma expressed as utilities and disability weights. Br. J. Dermatol. 2014; 171(6): 1443–1450. PubMed Abstract | Publisher Full Text
21. Alexandrescu DT: Melanoma costs: a dynamic model comparing estimated overall costs of various clinical stages. Dermatol. Online J. 2009; 15(11): 1. PubMed Abstract | Publisher Full Text
22. Reyes C, DaCosta BS, Linke R, et al.: The burden of metastatic melanoma: treatment patterns, healthcare use (utilization), and costs. Melanoma Res. 2013; 23(2): 159–166. Publisher Full Text
23. Montague M, Borland R, Sinclair C: Slip! Slop! Slap! and SunSmart, 1980-2000: Skin Cancer Control and 20 Years of Population-Based Campaigning. Health Educ. Behav. 2001; 28(3): 290–305. PubMed Abstract | Publisher Full Text
24. SPOT SKIN CANCER™ SEEKS A WORLD WITHOUT SKIN CANCER. American Academy of Dermatology; 2025. Reference Source Reference Source
25. Miles A, Waller J, Hiom S, et al.: SunSmart? Skin cancer knowledge and preventive behaviour in a British population representative sample. Health Educ. Res. 2005; 20(5): 579–585. PubMed Abstract | Publisher Full Text | Free Full Text
26. Armstrong A, Powell C, Powell R, et al.: Are we seeing the effects of public awareness campaigns? A 10-year analysis of Breslow thickness at presentation of malignant melanoma in the South West of England. J. Plast. Reconstr. Aesthet. Surg. 2014; 67(3): 324–330. PubMed Abstract | Publisher Full Text
27. Linsell L, Forbes L, Burgess C, et al.: Validation of a measurement tool to assess awareness of breast cancer. Eur. J. Cancer. 2010; 46(8): 1374–1381. PubMed Abstract | Publisher Full Text
28. Power E, Simon A, Juszczyk D, et al.: Assessing awareness of colorectal cancer symptoms: Measure development and results from a population survey in the UK. BMC Cancer. 2011; 11(1): 366. PubMed Abstract | Publisher Full Text | Free Full Text
29. Simon AE, Wardle J, Grimmett C, et al.: Ovarian and cervical cancer awareness: development of two validated measurement tools. J. Fam. Plann. Reprod. Health Care. 2012; 38(3): 167–174. PubMed Abstract | Publisher Full Text | Free Full Text
30. UK CR: The Cancer Awareness Measures (CAM). CRUK; 2014. Reference Source
31. Simon AE, Juszczyk D, Smyth N, et al.: Knowledge of lung cancer symptoms and risk factors in the U.K.: development of a measure and results from a population-based survey. Thorax. 2012; 67(5): 426–432. PubMed Abstract | Publisher Full Text
32. Stubbings S, Robb K, Waller J, et al.: Development of a measurement tool to assess public awareness of cancer. Br. J. Cancer. 2009; 101: S13–S17. Publisher Full Text
33. NICE C: Scenario: Referral for suspected skin cancer2025. Reference Source
34. NHS: What is melanoma skin cancer?2023. Reference Source
35. Australia CC: Clinical practice guidelines for the diagnosis and management of melanoma 2024Reference Source
36. Wilson L, Nix O, Janda M, et al.: Melanoma Cancer Awareness Measure (MCAM).2026, May 8. Publisher Full Text
37. Simon AE, Wardle J, Grimmett C, et al.: Ovarian and cervical cancer awareness: development of two validated measurement tools. J. Fam. Plann. Reprod. Health Care. 2012; 38(3): 167–174. PubMed Abstract | Publisher Full Text | Free Full Text
38. Wise E, Singh D, Moore M, et al.: Rates of skin cancer screening and prevention counseling by US medical residents. Arch. Dermatol. 2009; 145(10): 1131–1136. PubMed Abstract | Publisher Full Text
39. Moore MM, Geller AC, Zhang Z, et al.: Skin cancer examination teaching in US medical education. Arch. Dermatol. 2006; 142(4): 439–444. PubMed Abstract | Publisher Full Text
40. Little JM Jr, Hall MN, Pettice YJ: Teaching dermatology: too dependent on dermatologists? Fam. Med. 1993; 25(2): 92–94. PubMed Abstract
41. Stephenson A, From L, Cohen A, et al.: Family physicians' knowledge of malignant melanoma. J. Am. Acad. Dermatol. 1997; 37(6): 953–957. Publisher Full Text
42. Friedman KP, Whitaker-Worth DL, Grin C, et al.: Melanoma screening behavior among primary care physicians. Cutis. 2004; 74(5): 305–311. PubMed Abstract
43. Wormald J, Field S, Carter A, et al.: Awareness of melanoma in junior doctors, nurses and the general public: a comparative study. Field S, Wormald JCR, Carter A, Fishman G2014.
44. de Vere HI , Owen S, Amuzie A, et al.: Qualitative exploration of melanoma awareness in black people in the USA. BMJ Open. 2023; 13(1): e066967. Publisher Full Text
45. Bertolin M, Cercatto MC, Requena C, et al.: Awareness, Attitude, and Adherence to Preventive Measures in Patients at High Risk of Melanoma. A Cross-Sectional Study on 185 Patients. J. Cancer Educ. 2015; 30(3): 552–566. PubMed Abstract | Publisher Full Text
46. Chao LX, Patterson SSL, Rademaker AW, et al.: Melanoma Perception in People of Color: A Targeted Educational Intervention. Am. J. Clin. Dermatol. 2017; 18(3): 419–427. PubMed Abstract | Publisher Full Text
47. Touboul C: Methodology of the EDIFICE Melanoma survey. J. Eur. Acad. Dermatol. Venereol. 2015; 29 Suppl 2: 2–5. Publisher Full Text
48. Saiag P, Sassolas B, Mortier L, et al.: EDIFICE Melanoma survey: knowledge and attitudes on melanoma prevention and diagnosis. J. Eur. Acad. Dermatol. Venereol. 2015; 29(Suppl 2): 11–15. Publisher Full Text
49. Koumaki D, Papadakis M, Kouloumvakou S, et al.: Awareness, knowledge, and attitudes towards sun protection among patients with melanoma and atypical mole syndrome. World J Clin Oncol. 2022; 13(7): 587–598. PubMed Abstract | Publisher Full Text | Free Full Text
50. Buchanan Lunsford N, Berktold J, Holman DM, et al.: Skin cancer knowledge, awareness, beliefs and preventive behaviors among black and hispanic men and women. Prev. Med. Rep. 2018; 12: 203–209. PubMed Abstract | Publisher Full Text | Free Full Text
51. Oyebanjo E, Bushell F: A critical evaluation of the UK SunSmart campaign and its relevance to Black and minority ethnic communities. Perspect. Public Health. 2014; 134(3): 144–149. PubMed Abstract | Publisher Full Text
52. Halder RM, Ara CJ: Skin cancer and photoaging in ethnic skin. Dermatol. Clin. 2003; 21(4): 725–732. x. PubMed Abstract | Publisher Full Text
53. Pichon LC, Corral I, Landrine H, et al.: Sun-protection behaviors among African Americans. Am. J. Prev. Med. 2010; 38(3): 288–295. PubMed Abstract | Publisher Full Text
54. Council GM: MLA Content Map: General Medical Council2021. Reference Source
55. NBME FoSMBotUSa: USMLE Content Outline: Federation of State Medical Boards of the United States, Inc. (FSMB), and NBME2025. Reference Source
56. India MCo: Competency Based Undergraduate Curriculum For The Indian Medical Graduate2018. Reference Source
57. Wilson L, Nix O, Janda M, et al.: Development and validation of the Melanoma Cancer Awareness Measure- dataset. [Dataset]. University of Leeds; 2026. Publisher Full Text

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Author details Author details

¹ University of Leeds Leeds Institute of Health Sciences, Leeds, England, UK
² Princess Alexandra Hospital Centres for Health Research, The University of Queensland, Woolloongabba, Queensland, Australia
³ Dermatology Research Centre, The University of Queensland, Frazer Institute, Woolloongabba, Queensland, Australia

Liz Wilson
Roles: Data Curation, Formal Analysis, Investigation, Project Administration, Visualization, Writing – Original Draft Preparation

Oliver Nix
Roles: Data Curation, Formal Analysis, Investigation, Writing – Original Draft Preparation

Monika Janda
Roles: Conceptualization, Methodology, Writing – Review & Editing

H. Peter Soyer
Roles: Conceptualization, Methodology, Writing – Review & Editing

Samuel G. Smith
Roles: Conceptualization, Methodology, Project Administration, Supervision, Validation, Writing – Review & Editing

Sophie M. C. Green
Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Supervision, Validation, Writing – Review & Editing

Competing interests

HPS is a shareholder of MoleMap NZ Limited and e-derm consult GmbH and undertakes regular teledermatological reporting for both companies. HPS is a Medical Consultant for Canfield Scientific Inc and a Medical Advisor for First Derm. SGS has received financial compensation from Lilly for attending an expert advisory group. The author(s) declare(s) that there is no conflict of interest regarding the publication of this article.

Grant information

This project is funded by the National Institute for Health and Care Research (NIHR) by an Advanced Fellowship (Grant reference number: NIHR300588) and a Development and Skills Enhancement Award (Grant reference number: NIHR305132). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Professor Smith also acknowledges funding from a Yorkshire Cancer Research University Academic Fellowship (L389SS).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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https://doi.org/10.3310/nihropenres.14348.1

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Wilson L, Nix O, Janda M et al. Development and Validation of the Melanoma Cancer Awareness Measure [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:115 (https://doi.org/10.3310/nihropenres.14348.1)

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[1] 1. Heistein JB, Acharya U, Mukkamalla SKR, et al.: Treasure Island (FL) ineligible companies. Disclosure: Utkarsh Acharya declares no relevant financial relationships with ineligible companies. Disclosure: Shiva Kumar Mukkamalla declares no relevant financial relationships with ineligible companies.: StatPearls Publishing Copyright © 2025. StatPearls Publishing LLC; 2025.

[2] 2. Apalla Z, Lallas A, Sotiriou E, et al.: Epidemiological trends in skin cancer. Dermatol. Pract. Concept. 2017; 7(2): 1–6. PubMed Abstract | Publisher Full Text

[3] 3. Li Z, Fang Y, Chen H, et al.: Spatiotemporal trends of the global burden of melanoma in 204 countries and territories from 1990 to 2019: Results from the 2019 global burden of disease study. Neoplasia. 2022; 24(1): 12–21. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Cancer Research UK: Melanoma skin cancer statistics2015 [05/10/2022]. Reference Source

[5] 5. Matthews NH, Li WQ, Qureshi AA, et al.: Epidemiology of Melanoma.Ward WH, Farma JM, editors. Cutaneous Melanoma: Etiology and Therapy. Brisbane (AU): Codon Publications The Authors; 2017. Publisher Full Text

[6] 6. Conforti C, Zalaudek I: Epidemiology and Risk Factors of Melanoma: A Review. Dermatol. Pract. Concept. 2021; 11(Suppl 1): e2021161S. PubMed Abstract | Publisher Full Text

[7] 7. Karimkhani C, Green AC, Nijsten T, et al.: The global burden of melanoma: results from the Global Burden of Disease Study 2015. Br. J. Dermatol. 2017; 177(1): 134–140. Publisher Full Text

[8] 8. Sinclair C, Foley P: Skin cancer prevention in Australia. Br. J. Dermatol. 2009; 161(Suppl 3): 116–123. Publisher Full Text

[9] 9. Diffey B: Climate change, ozone depletion and the impact on ultraviolet exposure of human skin. Phys. Med. Biol. 2004; 49(1): R1–R11. PubMed Abstract | Publisher Full Text

[10] 10. Boniol M, Autier P, Boyle P, et al.: Cutaneous melanoma attributable to sunbed use: systematic review and meta-analysis. BMJ [Br. Med. J.]. 2012; 345: e4757. PubMed Abstract | Publisher Full Text | Free Full Text

[11] 11. Negin BP, Riedel E, Oliveria SA, et al.: Symptoms and signs of primary melanoma: important indicators of Breslow depth. Cancer. 2003; 98(2): 344–348. Publisher Full Text

[12] 12. Walter F, Webster A, Scott S, et al.: The Andersen Model of Total Patient Delay: a systematic review of its application in cancer diagnosis. J. Health Serv. Res. Policy. 2012; 17(2): 110–118. PubMed Abstract | Publisher Full Text | Free Full Text

[13] 13. Forbes LJ, Warburton F, Richards MA, et al.: Risk factors for delay in symptomatic presentation: a survey of cancer patients. Br. J. Cancer. 2014; 111(3): 581–588. PubMed Abstract | Publisher Full Text | Free Full Text

[14] 14. O’Shea SJ, Rogers Z, Warburton F, et al.: Which symptoms are linked to a delayed presentation among melanoma patients? A retrospective study. BMC Cancer. 2017; 17(1): 5. PubMed Abstract | Publisher Full Text | Free Full Text

[15] 15. Helvind NM, Brinch-Møller Weitemeyer M, Chakera AH, et al.: Stage-Specific Risk of Recurrence and Death From Melanoma in Denmark, 2008-2021: A National Observational Cohort Study of 25 720 Patients With Stage IA to IV Melanoma. JAMA Dermatol. 2023; 159(11): 1213–1222. PubMed Abstract | Publisher Full Text | Free Full Text

[16] 16. Garbe C, Keim U, Amaral T, et al.: Prognosis of Patients With Primary Melanoma Stage I and II According to American Joint Committee on Cancer Version 8 Validated in Two Independent Cohorts: Implications for Adjuvant Treatment. J. Clin. Oncol. 2022; 40(32): 3741–3749. PubMed Abstract | Publisher Full Text | Free Full Text

[17] 17. Bedikian AY, Johnson MM, Warneke CL, et al.: Prognostic Factors That Determine the Long-Term Survival of Patients with Unresectable Metastatic Melanoma. Cancer Investig. 2008; 26(6): 624–633. Publisher Full Text

[18] 18. Wilson MA, Zhong J, Rosenbaum BE, et al.: Impact of initial stage on metastatic melanoma survival. Melanoma Res. 2019; 29(3): 281–288. PubMed Abstract | Publisher Full Text | Free Full Text

[19] 19. Reitmajer M, Leiter U, Nanz L, et al.: Long-term survival of stage IV melanoma patients: evaluation on 640 melanoma patients entering stage IV between 2014 and 2017. J. Cancer Res. Clin. Oncol. 2024; 150(1): 15. PubMed Abstract | Publisher Full Text | Free Full Text

[20] 20. Tromme I, Devleesschauwer B, Beutels P, et al.: Health-related quality of life in patients with melanoma expressed as utilities and disability weights. Br. J. Dermatol. 2014; 171(6): 1443–1450. PubMed Abstract | Publisher Full Text

[21] 21. Alexandrescu DT: Melanoma costs: a dynamic model comparing estimated overall costs of various clinical stages. Dermatol. Online J. 2009; 15(11): 1. PubMed Abstract | Publisher Full Text

[22] 22. Reyes C, DaCosta BS, Linke R, et al.: The burden of metastatic melanoma: treatment patterns, healthcare use (utilization), and costs. Melanoma Res. 2013; 23(2): 159–166. Publisher Full Text

[23] 23. Montague M, Borland R, Sinclair C: Slip! Slop! Slap! and SunSmart, 1980-2000: Skin Cancer Control and 20 Years of Population-Based Campaigning. Health Educ. Behav. 2001; 28(3): 290–305. PubMed Abstract | Publisher Full Text

[24] 24. SPOT SKIN CANCER™ SEEKS A WORLD WITHOUT SKIN CANCER. American Academy of Dermatology; 2025. Reference Source Reference Source

[25] 25. Miles A, Waller J, Hiom S, et al.: SunSmart? Skin cancer knowledge and preventive behaviour in a British population representative sample. Health Educ. Res. 2005; 20(5): 579–585. PubMed Abstract | Publisher Full Text | Free Full Text

[26] 26. Armstrong A, Powell C, Powell R, et al.: Are we seeing the effects of public awareness campaigns? A 10-year analysis of Breslow thickness at presentation of malignant melanoma in the South West of England. J. Plast. Reconstr. Aesthet. Surg. 2014; 67(3): 324–330. PubMed Abstract | Publisher Full Text

[27] 27. Linsell L, Forbes L, Burgess C, et al.: Validation of a measurement tool to assess awareness of breast cancer. Eur. J. Cancer. 2010; 46(8): 1374–1381. PubMed Abstract | Publisher Full Text

[28] 28. Power E, Simon A, Juszczyk D, et al.: Assessing awareness of colorectal cancer symptoms: Measure development and results from a population survey in the UK. BMC Cancer. 2011; 11(1): 366. PubMed Abstract | Publisher Full Text | Free Full Text

[29] 29. Simon AE, Wardle J, Grimmett C, et al.: Ovarian and cervical cancer awareness: development of two validated measurement tools. J. Fam. Plann. Reprod. Health Care. 2012; 38(3): 167–174. PubMed Abstract | Publisher Full Text | Free Full Text

[30] 30. UK CR: The Cancer Awareness Measures (CAM). CRUK; 2014. Reference Source

[31] 31. Simon AE, Juszczyk D, Smyth N, et al.: Knowledge of lung cancer symptoms and risk factors in the U.K.: development of a measure and results from a population-based survey. Thorax. 2012; 67(5): 426–432. PubMed Abstract | Publisher Full Text

[32] 32. Stubbings S, Robb K, Waller J, et al.: Development of a measurement tool to assess public awareness of cancer. Br. J. Cancer. 2009; 101: S13–S17. Publisher Full Text

[33] 33. NICE C: Scenario: Referral for suspected skin cancer2025. Reference Source

[34] 34. NHS: What is melanoma skin cancer?2023. Reference Source

[35] 35. Australia CC: Clinical practice guidelines for the diagnosis and management of melanoma 2024Reference Source

[36] 36. Wilson L, Nix O, Janda M, et al.: Melanoma Cancer Awareness Measure (MCAM).2026, May 8. Publisher Full Text

[37] 37. Simon AE, Wardle J, Grimmett C, et al.: Ovarian and cervical cancer awareness: development of two validated measurement tools. J. Fam. Plann. Reprod. Health Care. 2012; 38(3): 167–174. PubMed Abstract | Publisher Full Text | Free Full Text

[38] 38. Wise E, Singh D, Moore M, et al.: Rates of skin cancer screening and prevention counseling by US medical residents. Arch. Dermatol. 2009; 145(10): 1131–1136. PubMed Abstract | Publisher Full Text

[39] 39. Moore MM, Geller AC, Zhang Z, et al.: Skin cancer examination teaching in US medical education. Arch. Dermatol. 2006; 142(4): 439–444. PubMed Abstract | Publisher Full Text

[40] 40. Little JM Jr, Hall MN, Pettice YJ: Teaching dermatology: too dependent on dermatologists? Fam. Med. 1993; 25(2): 92–94. PubMed Abstract

[41] 41. Stephenson A, From L, Cohen A, et al.: Family physicians' knowledge of malignant melanoma. J. Am. Acad. Dermatol. 1997; 37(6): 953–957. Publisher Full Text

[42] 42. Friedman KP, Whitaker-Worth DL, Grin C, et al.: Melanoma screening behavior among primary care physicians. Cutis. 2004; 74(5): 305–311. PubMed Abstract

[43] 43. Wormald J, Field S, Carter A, et al.: Awareness of melanoma in junior doctors, nurses and the general public: a comparative study. Field S, Wormald JCR, Carter A, Fishman G2014.

[44] 44. de Vere HI , Owen S, Amuzie A, et al.: Qualitative exploration of melanoma awareness in black people in the USA. BMJ Open. 2023; 13(1): e066967. Publisher Full Text

[45] 45. Bertolin M, Cercatto MC, Requena C, et al.: Awareness, Attitude, and Adherence to Preventive Measures in Patients at High Risk of Melanoma. A Cross-Sectional Study on 185 Patients. J. Cancer Educ. 2015; 30(3): 552–566. PubMed Abstract | Publisher Full Text

[46] 46. Chao LX, Patterson SSL, Rademaker AW, et al.: Melanoma Perception in People of Color: A Targeted Educational Intervention. Am. J. Clin. Dermatol. 2017; 18(3): 419–427. PubMed Abstract | Publisher Full Text

[47] 47. Touboul C: Methodology of the EDIFICE Melanoma survey. J. Eur. Acad. Dermatol. Venereol. 2015; 29 Suppl 2: 2–5. Publisher Full Text

[48] 48. Saiag P, Sassolas B, Mortier L, et al.: EDIFICE Melanoma survey: knowledge and attitudes on melanoma prevention and diagnosis. J. Eur. Acad. Dermatol. Venereol. 2015; 29(Suppl 2): 11–15. Publisher Full Text

[49] 49. Koumaki D, Papadakis M, Kouloumvakou S, et al.: Awareness, knowledge, and attitudes towards sun protection among patients with melanoma and atypical mole syndrome. World J Clin Oncol. 2022; 13(7): 587–598. PubMed Abstract | Publisher Full Text | Free Full Text

[50] 50. Buchanan Lunsford N, Berktold J, Holman DM, et al.: Skin cancer knowledge, awareness, beliefs and preventive behaviors among black and hispanic men and women. Prev. Med. Rep. 2018; 12: 203–209. PubMed Abstract | Publisher Full Text | Free Full Text

[51] 51. Oyebanjo E, Bushell F: A critical evaluation of the UK SunSmart campaign and its relevance to Black and minority ethnic communities. Perspect. Public Health. 2014; 134(3): 144–149. PubMed Abstract | Publisher Full Text

[52] 52. Halder RM, Ara CJ: Skin cancer and photoaging in ethnic skin. Dermatol. Clin. 2003; 21(4): 725–732. x. PubMed Abstract | Publisher Full Text

[53] 53. Pichon LC, Corral I, Landrine H, et al.: Sun-protection behaviors among African Americans. Am. J. Prev. Med. 2010; 38(3): 288–295. PubMed Abstract | Publisher Full Text

[54] 54. Council GM: MLA Content Map: General Medical Council2021. Reference Source

[55] 55. NBME FoSMBotUSa: USMLE Content Outline: Federation of State Medical Boards of the United States, Inc. (FSMB), and NBME2025. Reference Source

[56] 56. India MCo: Competency Based Undergraduate Curriculum For The Indian Medical Graduate2018. Reference Source

[57] 57. Wilson L, Nix O, Janda M, et al.: Development and validation of the Melanoma Cancer Awareness Measure- dataset. [Dataset]. University of Leeds; 2026. Publisher Full Text

Development and Validation of the Melanoma Cancer Awareness Measure

Abstract

Background

Methods

Results

Conclusions

Plain Language Summary

Keywords

Introduction

Methods

Patient and public involvement

Phase 1- generating questionnaire items

M-CAM sub-scales and scoring

Phase 2- validating the M-CAM

Study 1: reliability

Study 2: validity

Study 3: sensitivity to change

Results

Study 1: reliability

Study 2: validity

Study 3: sensitivity to change

Table 1. Descriptive analysis of all participants.

Table 2. Test-retest reliability.

Table 3. Construct validity.

Table 4. Sensitivity to change.

Discussion

Consent

Declaration

Data availability

Underlying data

Extended data

Acknowledgements

References

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