Variations in Patients’ Assessment of Chronic Illness Care across Organizational Models of Primary Healthcare: A Multilevel Cohort Analysis
Bibliographic record
Abstract
PURPOSE: To measure patients' assessment of chronic illness care and its variation across primary healthcare (PHC) models. METHODS: We recruited 776 patients with diabetes, heart failure, arthritis or chronic obstructive pulmonary disease from 33 PHC clinics. Face-to-face interviews, followed by a telephone interview at 12 months, were conducted using the Patient Assessment of Chronic Illness Care (PACIC). Multilevel regression was used in the analysis. RESULTS: The mean PACIC score was low at 2.5 on a scale of 1 to 5. PACIC scores were highest among patients affiliated with family medicine groups (mean, 2.78) and lowest for contact models (mean, 2.35). Patients with arthritis and older persons generally reported a lower assessment of chronic care. CONCLUSION: Family medicine groups represent an integrated model of PHC associated with higher levels of achievement in chronic care. Variations across PHC organizations suggest that some models are more appropriate for improving management of chronic illness.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".