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Record W3121018485 · doi:10.1097/jom.0000000000002133

Sun Protection Use at Work and Leisure by Outdoor Workers in Alberta, Canada

2020· article· en· W3121018485 on OpenAlexaffabout
Ela Rydz, Andrew Harper, Brandon Leong, Victoria H Arrandale, Sunil Kalia, Lindsay Forsman-Phillips, D. Linn Holness, Thomas Tenkate, Cheryl Peters

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsOccupational Cancer Research CentreSimon Fraser University
Fundersnot available
KeywordsSun protectionDemographicsWork (physics)Environmental healthSun exposureSkin cancerMedicineOffice workersSkin typeWork hoursDemographyGerontologyOperations managementCancerSociologyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the prevalence and determinants of sun protection behaviours among outdoors workers at work and leisure in Alberta, Canada. METHODS: We collected outdoor workers' demographics, skin cancer risk factors, job information, and sun habits at work and leisure using self-completed questionnaires. For both settings, we compared use of specific behaviours and modelled determinants of sun protection behaviour scores. RESULTS: At work, wearing a sleeved shirt (81% often/always) and hat (73%) were most prevalent, while seeking shade (12%) and applying sunscreen (36%) were least prevalent. Workers had higher sun protection scores at work than leisure. Hours spent outdoors was a strong determinant for both models. Additional leisure model predictors were eye colour, sex, skin type, and job group. CONCLUSIONS: Differences in behaviours across settings were observed and should be considered when developing solar UVR exposure reduction initiatives.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.025
GPT teacher head0.233
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes2
Has abstractyes

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