Rethinking Primary Care Delivery Models: Can Integrated Primary Care Teams Improve Care Experience?
Bibliographic record
Abstract
Background: Integrated Primary Care Teams (IPCTs) have four key characteristics (intensive interdisciplinary practice; advanced nursing practice with an expanded role; group practice; increased proximity and availability) aimed at strengthening primary care in Quebec, Canada. The purpose of this paper is to examine the care experience over time of patients who have an IPCT as their primary source of care. Methods: We used a quasi-experimental longitudinal design based on a pre-and-post administered survey at a 2-year interval without a control group. We measured patient-reported accessibility, continuity, comprehensiveness, responsiveness and outcomes of care. Results: Results showed that patients who were newly registered with an IPCT had a significant increase in reported care experience, whereas patients who have been registered with an IPCT for 2 years prior to the first round of data collection had already high reported care experience that was maintained over time. Moreover, linear regression models showed statistically significant different increases in the dimensions of care experience by site and patients' characteristics. Conclusions: Our results suggest that the IPCT model is tailored to the needs of its target populations, resulting in improved Patient Reported Experience Measures. These results imply that broader implementation of innovative and flexible community-based care models should be considered by policymakers.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".