Evaluating the implementation of collaborative teams in community family practice using the Primary Care Assessment Tool.
Bibliographic record
Abstract
OBJECTIVE: To examine patients' perceptions of care outcomes following the introduction of collaborative teams into community family practices. DESIGN: Cross-sectional, longitudinal study comprising 4 patient telephone surveys between 2007 and 2016, using random sampling of telephone records based on postal codes. SETTING: Ten WestView Primary Care Network (WPCN) clinics in Alberta, serving a suburban-rural population of approximately 89 000 and an aggregate clinic panel of 61 611 (in 2016). PARTICIPANTS: Adults aged 18 and older with a visit to a family physician in a WPCN clinic at least once in the previous 18 months. INTERVENTIONS: In 2006, WPCN implemented a decentralized and distributed collaborative team model, integrating nonphysician health care professionals into member clinics. MAIN OUTCOME MEASURES: The Primary Care Assessment Tool (PCAT) was used to evaluate standardized primary care delivery domains. Between-year changes were compared using ANOVA (analysis of variance). Clinic-level subgroup analyses were performed. RESULTS: < .001). The domains extent of affiliation, first-contact utilization, and coordination of information systems were unchanged. Ongoing care, coordination of care, comprehensiveness, family-centredness, community orientation, and cultural competence decreased. Except for in 2010, the 2 highest scoring clinics were non-participating solo practices; the lowest-scoring clinic was the one with the largest number of physicians. Across survey years, the PCAT summary score increased statistically significantly for 1 solo practice, remained consistent at an above-quality threshold for another, but decreased for all multi-physician clinics. Unattached patients (ie, those without a family doctor) scored the lowest. CONCLUSION: This study found that WPCN provides high-quality primary care overall, but that patient-perceived outcomes do not indicate global improvement concurrent with team-based initiatives. Decreased standardization of the distributed model likely influenced study-observed variations in clinic performance. Future research should identify clinic and team characteristics that benefit most from team-based care and factors that explain solo practices outperforming models of team-based 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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".