Power analysis in health policy and systems research: a guide to research conceptualisation
Bibliographic record
Abstract
Power is a growing area of study for researchers and practitioners working in the field of health policy and systems research (HPSR). Theoretical development and empirical research on power are crucial for providing deeper, more nuanced understandings of the mechanisms and structures leading to social inequities and health disparities; placing contemporary policy concerns in a wider historical, political and social context; and for contributing to the (re)design or reform of health systems to drive progress towards improved health outcomes. Nonetheless, explicit analyses of power in HPSR remain relatively infrequent, and there are no comprehensive resources that serve as theoretical and methodological starting points. This paper aims to fill this gap by providing a consolidated guide to researchers wishing to consider, design and conduct power analyses of health policies or systems. This practice article presents a synthesis of theoretical and conceptual understandings of power; describes methodologies and approaches for conducting power analyses; discusses how they might be appropriately combined; and throughout reflects on the importance of engaging with positionality through reflexive praxis. Expanding research on power in health policy and systems will generate key insights needed to address underlying drivers of health disparities and strengthen health systems for all.
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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 | 0.179 | 0.162 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.006 | 0.076 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".