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Record W4376129550 · doi:10.1097/jom.0000000000002879

“It's Just a Checklist”

2023· article· en· W4376129550 on OpenAlexaffabout
Alexa Adams, C. Gladson Clifford Joe, N. Klinger, Erika Laforest, Janki Shankar, Shu‐Ping Chen

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsThematic analysisWorkforceChecklistQualitative researchOccupational safety and healthTraining (meteorology)PsychologyMedicineNursingBusinessApplied psychologyPolitical scienceSociologyGeography

Abstract

fetched live from OpenAlex

OBJECTIVES: New immigrant workers (NIWs) are overrepresented in hazardous workplaces. Sufficient Occupational Health and Safety (OH&S) training could minimize workplace injuries. This study aims to identify the current status of OH&S and training for NIWs in Canada. METHODS: Generic qualitative research was conducted. Seven NIWs and nine service providers were interviewed to understand OH&S issues, perceptions on rules and regulations, and expectations for training. Thematic analysis was used for data analysis. RESULTS: Four themes that affect OH&S for NIWs include attitudes toward safety and training, personal barriers, Canadian workplace culture, and macrolevel interconnected systems. Three needs on OH&S training are increasing accessibility, ensuring full understanding, and building confidence. CONCLUSIONS: Current training does not mitigate safety risks in workplaces, and NIWs do not feel empowered to exercise their rights. New training protocols would be beneficial to equip NIWs to enter the workforce.

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)0.015
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.230
Threshold uncertainty score0.457

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.043
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0060.004
Scholarly communication0.0030.004
Open science0.0040.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0310.013

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 head0.178
GPT teacher head0.499
Teacher spread0.321 · 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
GenreCommentary

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

Citations3
Published2023
Admission routes2
Has abstractyes

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