Citizens' Quality Councils: An Innovative Mechanism for Monitoring and Providing Social Endorsement of Healthcare Providers' Performance?
Bibliographic record
Abstract
In recent years, under the influence of continuing improvement and total quality strategies, efforts to improve the quality of healthcare have been generated from within each healthcare organization. External mechanisms, such as accreditation, that drive quality improvement from without, have existed for much longer. However, these accreditation systems incorporated the need to demonstrate the existence of continuing improvement processes as a standard barely 10 years ago; thus, the external mechanism included the development of internal processes as yet another requirement. As Dobrow, Langer, Angus and Sullivan state in the lead article, the existence of a whole evidence-based culture that has spread the concern about quality is beyond doubt; I would add that it has also intensified this concern. Several factors have contributed to this trend, which now seems irreversible. On the one hand, as the paper points out, one of these factors is the growing requirement to allocate resources according to performance. On the other, there is the growing evidence of errors committed by health systems that cause harm to patients. The latter has created increasing demand for reliable information, conceived not only to allow the detection of these situations, but also to invest greater reliability in the health systems in the eyes of patients and general public. In both cases, however, the question remains: Who defines and who measures quality levels in such a way that the information is credible?
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 | 0.125 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".