Barriers and Facilitators to Sun Protection: A Proposal for a Skin Cancer Public Health Prevention Campaign in Atlantic Canada
Bibliographic record
Abstract
BACKGROUND: Regions of Atlantic Canada have the highest incidence rates of cutaneous melanoma in Canada. Despite its preventable nature through sun-safe behaviours, region-specific public health strategies remain limited. OBJECTIVE: To explore perceived barriers and facilitators to sun protection in Atlantic Canada, using the Capability, Opportunity, Motivation-Behaviour (COM-B) model to guide intervention development. METHODS: We conducted a qualitative content analysis of 22 focus groups comprising 95 participants across four Atlantic provinces. Transcripts were analyzed using the COM-B model within the Behaviour Change Wheel framework, facilitated by MAXQDA software. Themes related to behavioural capability, environmental opportunity, and motivational factors were identified, with proposed interventions and policies aligned to these domains. RESULTS: Barriers included challenges in sunscreen application, limited knowledge, chemical concerns, financial barriers, insufficient infrastructure/shade in public spaces, and occupation-specific barriers. Facilitators encompassed heightened awareness following personal experiences with skin cancer, social role modelling, and habit formation. Participants endorsed locally sourced educational and enabling strategies over coercive approaches. Suggested policies included improving sunscreen affordability, integrating sun protection into workplace guidelines, enhancing access to public shade, and leveraging mass media for targeted/region-centred campaigns. CONCLUSION: Effective sun protection initiatives in Atlantic Canada should be grounded in the COM-B model, addressing individual capabilities, environmental opportunities, and motivational drivers. A multifaceted, community-informed strategy is needed and preferred to sustainably reduce melanoma risk in this high-incidence region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".