Helping primary care teams emerge through a quality improvement program. Family Practice,30(2
Bibliographic record
Abstract
Background. Approaches to improving the quality of health care recognize the need for systems and cultures that facilitate optimal care. Interpersonal relationships and dynamics are a key fac-tor in transforming a system to one that can achieve quality. The Quality in Family Practice (QIFP) program encompasses clinical and practice management using a comprehensive tool of family practice indicators. Objective. The objective of this study was to explore and describe the views of staff regarding changes in the clinical practice environment at two affiliated academic primary care clinics (com-prising one Family Health Team, FHT) who participated in QIFP. Methods. An FHT in Hamilton, Canada, worked through the quality tool in 2008/2009. A qualita-tive exploratory case study approach was employed to examine staff perceptions of the process of participating. Semi-structured interviews were conducted in early 2010 with 43 FHT staff with representation from physicians, nurses, allied health professionals, support staff and managers. Interviews were audio-taped and transcribed verbatim. A modified template approach was used for coding, with a complexity theory perspective of analysis. Results. Themes included importance of leadership, changes to practice environment, changes to
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".