Legitimizing new practices in primary health care
Bibliographic record
Abstract
BACKGROUND: Finding ways to reinvent primary health care is imperative. One way is to change practices from a physician-focused model to an interdisciplinary team approach where other health professionals (nurses, nurse practitioners, dieticians, rehabilitation therapists, and other qualified primary care providers) collectively take on much stronger roles-often providing services instead of the physician. Health care policy makers and professionals agree that these new practices are a good idea, and yet they have not been widely adopted. PURPOSE: Our goal was to understand how new interdisciplinary practices became legitimized as the new accepted working standards. METHODOLOGY: We conducted a qualitative, longitudinal comparative case study of 8 primary health care innovation sites established to provide services through interdisciplinary teams. We followed changes in practices over a 3-year period by conducting 150 interviews with professionals and managers across the 8 sites. FINDINGS: At the end of 3 years, new practices were adopted in 5 of the sites, but in 3 sites, they were not. We explain the differences by identifying a series of strategies used by managers in the successful sites and compare them with those used in the other 3 sites. Strategies used in the successful sites were (a) gaining full engagement, (b) enticing people to try new practices, (c) encouraging structured disagreement, and (d) staying focused on overall goals. PRACTICE IMPLICATIONS: Managers of health care change initiatives must gain buy-in from professionals, but that is not enough. They must also facilitate trying the new practices as soon as possible. Open disagreement should be carefully encouraged, but any concerns must also be successfully addressed. Finally, managers must keep professionals focused on the overall goals of change rather than allowing paralysis in response to external events.
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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 | 0.049 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.050 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".