Domains and processes for institutionalizing evidence-informed health policy-making: a critical interpretive synthesis
Bibliographic record
Abstract
BACKGROUND: While calls for institutionalization of evidence-informed policy-making (EIP) have become stronger in recent years, there is a paucity of methods that governments and organizational knowledge brokers can use to sustain and integrate EIP as part of mainstream health policy-making. The objective of this paper was to conduct a knowledge synthesis of the published and grey literatures to develop a theoretical framework with the key features of EIP institutionalization. METHODS: We applied a critical interpretive synthesis (CIS) that allowed for a systematic, yet iterative and dynamic analysis of heterogeneous bodies of literature to develop an explanatory framework for EIP institutionalization. We used a "compass" question to create a detailed search strategy and conducted electronic searches to identify papers based on their potential relevance to EIP institutionalization. Papers were screened and extracted independently and in duplicate. A constant comparative method was applied to develop a framework on EIP institutionalization. The CIS was triangulated with the findings of stakeholder dialogues that involved civil servants, policy-makers and researchers. RESULTS: We identified 3001 references, of which 88 papers met our eligibility criteria. This CIS resulted in a definition of EIP institutionalization as the "process and outcome of (re-)creating, maintaining and reinforcing norms, regulations, and standard practices that, based on collective meaning and values, actions as well as endowment of resources, allow evidence to become-over time-a legitimate and taken-for-granted part of health policy-making". The resulting theoretical framework comprised six key domains of EIP institutionalization that capture both structure and agency: (1) governance; (2) standards and routinized processes; (3) partnership, collective action and support; (4) leadership and commitment; (5) resources; and (6) culture. Furthermore, EIP institutionalization is being achieved through five overlapping stages: (i) precipitating events; (ii) de-institutionalization; (iii) semi-institutionalization (comprising theorization and diffusion); (iv) (re)-institutionalization; and (v) renewed de-institutionalization processes. CONCLUSIONS: This CIS advances the theoretical and conceptual discussions on EIP institutionalization, and provides new insights into an evidence-informed framework for initiating, strengthening and/or assessing efforts to institutionalize EIP.
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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 | 0.495 | 0.607 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.075 | 0.042 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.032 | 0.028 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".