Divergent notions of “quality” in healthcare policy implementation: a framing perspective
Bibliographic record
Abstract
PURPOSE: This paper examines how "quality" was framed in the design and implementation of a policy to reform hospital funding and associated care delivery. The aims of the study were: (1) To describe how government policy-makers who designed the policy and managers and clinicians who implemented the policy framed the concept of "quality" and (2) To explore how frames of quality and the framing process may have influenced policy implementation. DESIGN/METHODOLOGY/APPROACH: The authors conducted a secondary analysis of data from a qualitative case study involving semi-structured interviews with 45 purposefully selected key informants involved in the design and implementation of the quality-based procedures policy in Ontario, Canada. The authors used framing theory to inform coding and analysis. FINDINGS: The authors found that policy designers perpetuated a broader frame of quality than implementers who held more narrow frames of quality. Frame divergence was further characterized by how informants framed the relationship between clinical and financial domains of quality. Several environmental and organizational factors influenced how quality was framed by implementers. ORIGINALITY/VALUE: As health systems around the world increasingly implement new models of governance and financing to strengthen quality of care, there is a need to consider how "quality" is framed in the context of these policies and with what effect. This is the first framing analysis of "quality" in health policy.
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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 | 0.073 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.015 | 0.090 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".