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Record W2975009613 · doi:10.1097/der.0000000000000278

Preventing Occupational Skin Disease: A Review of Training Programs

2017· review· en· W2975009613 on OpenAlexaffvenue
Bethany Zack, Victoria H Arrandale, D. Linn Holness

Bibliographic record

VenueDermatitis · 2017
Typereview
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsUniversity of TorontoCancer Care OntarioOccupational Cancer Research Centre
Fundersnot available
KeywordsMedicineGeneralizability theoryContext (archaeology)Health careDiseaseNursingFamily medicinePathology

Abstract

fetched live from OpenAlex

Occupational contact dermatitis (OCD) is a common occupational disease that impacts a variety of worker groups. Skin protection and disease prevention training programs have shown promise for improving prevention practices and reducing the incidence of OCD. This review details the features of training programs for primary prevention of OCD and identifies gaps in the literature. Twelve studies were identified for in-depth review: many studies included wet workers employed in health care, hairdressing, cleaning, and food preparation; 1 program featured manufacturing workers. Few programs provided content on allergic contact dermatitis, and only 1 was evaluated for long-term effectiveness. Effective programs were similar in content, delivery method, and timing and were characterized by industry specificity, multimodal learning, participatory elements, skin care resource provision, repeated sessions, and management engagement. Long-term effectiveness, generalizability beyond OCD, workplace health and safety culture impact, and translation of programs in the North American context represent areas for future research.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.138
GPT teacher head0.398
Teacher spread0.260 · 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 designNot applicable
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

Citations25
Published2017
Admission routes2
Has abstractyes

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