Providing high-quality care in primary care settings
Bibliographic record
Abstract
Objective To gain a deeper understanding of how primary care (PC) practices belonging to different models manage resources to provide high-quality care. Design Multiple-case study embedded in a cross-sectional study of a random sample of 37 practices. Setting Three regions of Quebec. Participants Health care professionals and staff of 5 PC practices. Methods Five cases showing above-average results on quality-of-care indicators were purposefully selected to contrast on region, practice size, and PC model. Data were collected using an organizational questionnaire; the Team Climate Inventory, which was completed by health care professionals and staff; and 33 individual interviews. Detailed case histories were written and thematic analysis was performed. Main findings The core common feature of these practices was their ongoing effort to make trade-offs to deliver services that met their vision of high-quality care. These compromises involved the same 3 areas, but to varying degrees depending on clinic characteristics: developing a shared vision of high-quality care; aligning resource use with that vision; and balancing professional aspirations and population needs. The leadership of the physician lead was crucial. The external environment was perceived as a source of pressure and dilemmas rather than as a source of support in these matters. Conclusion Irrespective of their models, PC practices’ pursuit of high-quality care is based on a vision in which accessibility is a key component, balanced by appropriate management of available resources and of external environment expectations. Current PC reforms often create tensions rather than support PC practices in their pursuit of high-quality care.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".