The contribution of case study research to knowledge of how to improve quality of care
Bibliographic record
Abstract
BACKGROUND: Efforts to improve the implementation of effective practice and to speed up improvements in quality and patient safety continue to pose challenges for researchers and policy makers. Organisational research, and, in particular, case studies of quality improvement, offer methods to improve understanding of the role of organisational and microsystem contexts for improving care and the development of theories which might guide improvement strategies. METHODS: This paper reviews examples of such research and details the methodological issues in constructing and analysing case studies. Case study research typically collects a wide array of data from interviews, documents and other sources. CONCLUSION: Advances in methods for coding and analysing these data are improving the quality of reports from these studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.174 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.018 | 0.039 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".