Institutional Linkages Between National Social Health Protection And Occupational Health Services Systems: A Scoping Review v1
Bibliographic record
Abstract
The impact of globalization and industrialization in the last decade has been characterized by a persistence of informality and an increase in precarious jobs increasing workers’ exposure to occupational health risks. Technological advances and the COVID-19 pandemic have also accelerated participation in the gig and platform economy, giving rise to new forms of employment for which neither national social health protection (SHP) systems nor occupational health services (OHS) are adequately adapted. In many LMICs but also high-income countries, OHS remain bound by the employer-employee relationship, often leaving workers engaged in the informal, platform and gig economies without access to OHS. Similarly, this group remains disproportionately excluded from SHP coverage. Recent evidence has also shown the tendency for national SHP systems and workers to absorb the financial burden of occupational disease, despite the existence of workers’ compensation mechanisms. This signals a preponderance of curative care over preventative care with important financial ramifications for national SHP systems but also for workers who face the financial and health risks of preventable diseases and accidents. The extension of SHP and OHS coverage to workers in all forms of employment is thus a joint priority for both OHS and SHP systems. In this paper, we argue that the establishment of robust interinstitutional linkages can strengthen both systems and extend coverage of both SHP and OHS. Limited evidence of such interinstitutional linkages in countries such as Canada and Finland supports this argument. However, limited evidence exists elsewhere, particularly in LMICs. This scoping review aims to document existing institutional linkages between SHP and OHS systems globally. To do so, it asks: 1) In which countries can we identify interinstitutional linkages? and 2) What is the nature of such linkages? This study is based on the scoping methodology of Arksey and O’Malley. Relevant databases and grey literature will be searched using a search strategy developed by a librarian at Université de Laval. A double-blind selection of papers will be conducted based on defined inclusion and exclusion criteria. Finally, deductive thematic synthesis is used to present the results and a typology of linkages is developed. To the best of our knowledge, this study is the first to provide a systematic mapping of interinstitutional linkages between OHS and SHP systems globally. A better understanding of such linkages will allow for the documentation of best practices that can guide the strengthening of both systems in the face of current global crises.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".