Implementing primary care reform : barriers and facilitators
Bibliographic record
Abstract
Strong primary health care systems are the foundation of effective health care. Several countries have attempted to reform primary care delivery in the past few years, with variable results. In Implementing Primary Care Reform the authors examine the barriers to, and facilitators of, such reform in Canada, New Zealand, and the United Kingdom, from political, economic, organizational, and clinical perspectives. The authors emphasize the importance of primary care in improving health, increasing cost-effectiveness, and promoting social equity. The contributors include: Marie-Dominique Beaulieu (University of Montreal), Raisa Deber (University of Toronto), Michael Decter (National Health Council), Cathy Fooks (Health Network Canadian Policy Research Networks), Brian Hutchinson (McMaster University), Antonia Maioni (McGill University), Nick Mays (London School of Hygiene and Tropical Medicine), Bonnie Sibbald (University of Manchester), Barbara Starfield (John Hopkins University), and Carolyn Tuohy (University of Toronto).
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 | 0.066 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".