Assessing integrative prevention at work: A scoping review
Bibliographic record
Abstract
BackgroundIntegrative prevention at work is a promising avenue to better prevent occupational injuries and manage prolonged incapacity in a changing world of work. Integrative prevention at work can be operationalized using its five defining attributes: (1) holistic vision of health, (2) common understanding of the purpose of integrative prevention, (3) communication among stakeholders, (4) collaboration among stakeholders, and (5) coordination of preventive action. An assessment tool for these characteristics would be a valuable resource for organizations seeking to improve their approach to prevention. Namely, it would allow organizations to assess the presence of integrative prevention at work in their environment and enhance their ability to implement it.ObjectiveThis study aimed to describe the evaluation tools assessing attributes of integrative prevention at work.MethodsThis scoping review followed a five-step process: 1) identifying the research question, 2) identifying relevant documents, 3) selecting documents, 4) extracting the data, and 5) examining, synthesizing, and reporting the results.ResultsTwelve evaluation tools were identified assessing one or more attributes of integrative prevention at work. Descriptive elements are provided for each tool (e.g., its purpose, the attribute(s) it assesses, and its metrological properties). Our study suggests that communication among stakeholders and collaboration among stakeholders are the attributes that are the most assessed by the evaluation tools.ConclusionsThis study provides the first comprehensive and detailed overview of the extant tools currently being used to assess the attributes of integrative prevention at work. None can assess all five attributes on a unified scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.170 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.057 | 0.042 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".