Promoting good policy for leadership and governance of health related rehabilitation: a realist synthesis
Bibliographic record
Abstract
BACKGROUND: Good governance may result in strengthened performance of a health system. Coherent policies are essential for good health system governance. The overall aim of this research is to provide the best available scientific evidence on principles of good policy related leadership and governance of health related rehabilitation services in less resourced settings. This research was also conducted to support development of the World Health Organization's (WHO) Guidelines on health related rehabilitation. METHODS: An innovative study design was used, comprising two methods: a systematic search and realist synthesis of literature, and a Delphi survey of expert stakeholders to refine and triangulate findings from the realist synthesis. In accordance with Pawson and Tilley's approach to realist synthesis, we identified context mechanism outcome pattern configurations (CMOCs) from the literature. Subsequently, these CMOCs were developed into statements for the Delphi survey, whereby 18 expert stakeholders refined these statements to achieve consensus on recommendations for policy related governance of health related rehabilitation. RESULTS: Several broad principles emerged throughout formulation of recommendations: participation of persons with disabilities in policy processes to improve programme responsiveness, efficiency, effectiveness, and sustainability, and to strengthen service-user self-determination and satisfaction; collection of disaggregated disability statistics to support political momentum, decision-making of policymakers, evaluation, accountability, and equitable allocation of resources; explicit promotion in policies of access to services for all subgroups of persons with disabilities and service-users to support equitable and accessible services; robust inter-sectoral coordination to cultivate coherent mandates across governmental departments regarding service provision; and 'institutionalizing' programmes by aligning them with preexisting Ministerial models of healthcare to support programme sustainability. CONCLUSIONS: Alongside national policymakers, our policy recommendations are relevant for several stakeholders, including service providers and service-users. This research aims to provide broad policy recommendations, rather than a strict formula, in acknowledgement of contextual diversity and complexity. Accordingly, our study proposes general principles regarding optimal policy related governance of health related rehabilitation in less resourced settings, which may be valuable across diverse health systems and contexts.
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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 | 0.119 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".