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Record W2041003943 · doi:10.1097/jom.0b013e3181b34f60

A Systematic Review of Occupational Health and Safety Interventions With Economic Analyses

2009· review· en· W2041003943 on OpenAlexaff
Emile Tompa, Roman Dolinschi, Claire de Oliveira, Emma Irvin

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2009
Typereview
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitute for Work & Health
Fundersnot available
KeywordsPsychological interventionSystematic reviewIntervention (counseling)Occupational safety and healthGrey literatureInclusion (mineral)Quality (philosophy)Evidence-based medicineMEDLINEMedicineAlternative medicinePsychologyNursingPolitical sciencePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: We reviewed the occupational health and safety intervention literature to synthesize evidence on financial merits of such interventions. METHODS: A literature search included journal databases, existing systematic reviews, and studies identified by content experts. Studies meeting inclusion criteria were assessed for quality. Evidence was synthesized within industry-intervention type clusters. RESULTS: We found strong evidence that ergonomic and other musculoskeletal injury prevention interventions in manufacturing and warehousing are worth undertaking in terms of their financial merits. We also found strong evidence that multisector disability management interventions are worth undertaking. CONCLUSIONS: While the economic evaluation of interventions in this literature warrants further expansion, we found a sufficient number of studies to identify strong, moderate, and limited evidence in certain industry-intervention clusters. The review also provided insights into how the methodological quality of economic evaluations in this literature could be improved.

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)0.033
metaresearch head score (Gemma)0.154
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 score0.033
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.154
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0140.013
Bibliometrics0.0200.018
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.261
GPT teacher head0.568
Teacher spread0.307 · 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

Citations91
Published2009
Admission routes1
Has abstractyes

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