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Record W2100693653 · doi:10.1186/1756-0500-7-10

Interventions to decrease skin cancer risk in outdoor workers: update to a 2007 systematic review

2014· review· en· W2100693653 on OpenAlexaff
Caitlin Horsham, Josephine Auster, Marguerite C. Sendall, Melissa Stoneham, Philippa Youl, Phil Crane, Thomas Tenkate, Monika Janda, Michael G. Kimlin

Bibliographic record

VenueBMC Research Notes · 2014
Typereview
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSkin cancerPsychological interventionMedicineSystematic reviewEnvironmental healthIntervention (counseling)Inclusion (mineral)CancerMEDLINENursingPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Outdoor workers are at high risk of harmful ultraviolet radiation exposure and are identified as an at risk group for the development of skin cancer. This systematic evidence based review provides an update to a previous review published in 2007 about interventions for the prevention of skin cancer in outdoor workers. RESULTS: This review includes interventions published between 2007-2012 and presents findings about sun protection behaviours and/or objective measures of skin cancer risk. Six papers met inclusion criteria and were included in the review. Large studies with extended follow-up times demonstrated the efficacy of educational and multi-component interventions to increase sun protection, with some higher use of personal protective equipment such as sunscreen. However, there is less evidence for the effectiveness of policy or specific intervention components. CONCLUSIONS: Further research aimed at improving overall attitudes towards sun protection in outdoor workers is needed to provide an overarching framework.

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)0.012
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.043
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.283
GPT teacher head0.546
Teacher spread0.263 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations74
Published2014
Admission routes1
Has abstractyes

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