Bibliographic record
Abstract
This paper aims to draw attention to the social and micropolitical dimensions of attempting to implement improvements within healthcare organisations. It is argued that quality improvement initiatives, like other forms of organisational innovation, will fail unless they are conceived and implemented in such a way as to take into account the pattern of interests, values and power relationships that surround them. Drawing on examples, it is suggested that innovators can intervene more successfully if they understand how the benefits and costs of interventions are likely to be distributed among stakeholders within their setting, how different but equally legitimate value sets may structure peoples' understanding of them and how the nature of the interventions themselves (and, in particular, the shape of their hard core and soft periphery) might provide scope for redesigning or adapting interventions in ways that are likely to make them both more effective and politically feasible.
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 | 0.116 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.007 | 0.112 |
| Scholarly communication | 0.028 | 0.061 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".