What Encourages Sun Protection among Outdoor Workers from Four Industries?
Bibliographic record
Abstract
OBJECTIVES: We aimed to identify current practice of sun protection and factors associated with effective use in four outdoor worker industries in Queensland, Australia. METHODS: Workplaces in four industries with a high proportion of outdoor workers (building/construction, rural/farming, local government, and public sector industries) were identified using an online telephone directory, screened for eligibility, and invited to participant via mail (n=15, recruitment rate 37%). A convenience sample of workers were recruited within each workplace (n=162). Workplaces' sun protective policies and procedures were identified using interviews and policy analysis with workplace representatives, and discussion groups and computer-assisted telephone interviews with workers. Personal characteristics and sun protection knowledge, attitudes and behaviors were collated and analysed. RESULTS: Just over half the workplaces had an existing policy which referred to sun protection (58%), and most provided at least some personal protective equipment (PPE), but few scheduled work outside peak sun hours (43%) or provided skin checks (21%). Several worker and workplace characteristics were associated with greater sun protection behaviour among workers, including having received education on the use of PPE (p<0.001), being concerned about being in the sun (p=0.002); and working in a smaller workplace (p=0.035). CONCLUSIONS: Uptake of sun protection by outdoor workers is affected by a complex interplay of both workplace and personal factors, and there is a need for effective strategies targeting both the workplace environment and workers' knowledge, attitudes and behaviors to decrease harmful sun exposure further.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".