Bibliographic record
Abstract
Sullivan et al. have captured several important themes. One of the reasons that healthcare has been slow to adopt a culture of quality has been that it has taken a long time to recognize that quality is a continuous journey along several dimensions. Following advances in the early 1990s on appropriateness and effectiveness, there has been a decade-long preoccupation with accessibility that still remains an issue. Patient-centredness is one of the most recent dimensions to receive attention, and the overall goal of quality - improved patient outcomes - needs considerable work. Measurement and reporting are fundamental to quality improvement, but the provincial and territorial governments have not lived up to their Health Accord commitments to regular reporting on common indicators. At least six provinces have established health quality councils, but it remains to be seen if this bottom-up approach will lead to a common reporting framework that will support benchmarking. Canada would likely benefit from a pan-Canadian approach to innovation in healthcare quality.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.014 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.083 | 0.057 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".