Relevance and use of health policy, health systems and health services research for strengthening rehabilitation in real-life settings: methodological considerations
Bibliographic record
Abstract
Research on health policy, systems, and services (HPSSR) has seen significant growth in recent decades and received increasing attention in the field of rehabilitation. This growth is driven by the imperative to effectively address real-life challenges in complex healthcare settings. A recent resolution on ‘Strengthening rehabilitation in health systems’ adopted by the World Health Assembly emphasizes the need to support societal health goals related to rehabilitation, particularly to promote high-quality rehabilitation research, including HPSSR. This conceptual paper, discussed with the participants in the 5th Cochrane Rehabilitation Methodological Meeting held in Milan on September 2023, outlines study designs at diverse levels at which HPSSR studies can be conducted: the macro, meso, and micro levels. It categorizes research questions into four types: those framed from the perspective of policies, healthcare delivery organizations or systems, defined patient or provider populations, and important data sources or research methods. Illustrative examples of appropriate methodologies are provided for each type of research question, demonstrating the potential of HPSSR in shaping policies, improving healthcare delivery, and addressing patient and provider perspectives. The paper concludes by discussing the applicability, usefulness, and implementation of HPSSR findings, and the importance of knowledge translation strategies, drawing insights from implementation science. The goal is to facilitate the integration of research findings into everyday clinical practice to bridge the gap between research and practice in rehabilitation.
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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 | 0.695 | 0.808 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.030 | 0.032 |
| Open science | 0.014 | 0.019 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".