Applying theories to better understand socio-political challenges in implementing evidence-based work disability prevention strategies
Bibliographic record
Abstract
PURPOSE: This article explores and applies theories for analyzing socio-political aspects of implementation of work disability prevention (WDP) strategies. METHOD: For the analysis, theories from political science are explained and discussed in relation to case examples from three jurisdictions (Sweden, Brazil and Québec). RESULTS: Implementation of WDP strategies may be studied through a conceptual framework that targets: (1) the institutional system in which policy-makers and other stakeholders reside; (2) the ambiguity and conflicts regarding what to do and how to do it; (3) the bounded rationality, path dependency and social systems of different stakeholders; and (4) coalitions formed by different stakeholders and power relations between them. In the case examples, the design of social insurance systems, the access to and infrastructure of healthcare systems, labor market policies, employers' level of responsibility, the regulatory environment, and the general knowledge of WDP issues among stakeholders played different roles in the implementation of policies based on scientific evidence. CONCLUSIONS: Future research may involve participatory approaches focusing on building coalitions and communities of practice with policy-makers and stakeholders, in order to build trust, facilitate cooperation, and to better promote evidence utilization. Implications for Rehabilitation Implementation of work disability prevention policies are subject to contextual influences from the socio-political setting and from relationships between stakeholders Stakeholders involved in implementing strategies are bound to act based on their interests and previous courses of action To promote research uptake on the policy level, stakeholders and researchers need to engage in collaboration and translational activities Political stakeholders at the government and community levels need to be more directly involved as partners in the production and utilization of evidence.
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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 | 0.090 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.010 | 0.073 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".