Integrative prevention and coordinated action toward primary, secondary and tertiary prevention in workplaces: A scoping review
Bibliographic record
Abstract
BACKGROUND: Integrated approaches are valued in several occupational health strategic programmatic orientations. A better understanding of the use of integrative prevention in coordinating measures is needed to develop its use in workplaces. OBJECTIVE: Identify workplace integrative prevention approaches and definitions of prevention (primary, secondary and tertiary) in the literature. METHODS: A scoping review was conducted following Arksey and O'Malley (2005). The literature search was carried out in three databases without date restrictions. In order to be retained, the articles needed to address at least two levels of prevention using an integrative approach in a workplace setting. A qualitative analysis was conducted. RESULTS: The review yielded 16 published articles between 1995 and 2017. The articles addressed mental health, musculoskeletal disorder prevention and comprehensive approaches. Integrative prevention approaches are diverse and are not always named as such. Prevention definitions are not homogenous. CONCLUSIONS: This review identified some of the integrative prevention characteristics aimed at coordinated action for prevention in the workplace and to clarify measures taken at different levels of prevention. Further studies are needed to elaborate on the implementation of integrative prevention in the workplace.
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 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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".