Diabetes care: Comparison of patients' and healthcare professionals' assessment using the PACIC instrument
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVE: Whereas the Patient Assessment of Chronic Illness Care (PACIC) instrument measures the extent to which care received by patients is congruent with the Chronic Care Model, the 5As model emphasizes self-management and community resources, 2 key components of the Chronic Care Model. We aimed at comparing evaluation of diabetes care, as reported by patients with diabetes and healthcare professionals (HCPs), using these instruments. METHODS: Two independent samples, patients with diabetes (n = 395) and HCPs (including primary and secondary care physicians and nurses; n = 287), responded to the 20-item PACIC and the six 5As model questions. The PACIC-5A (questions scored on a 5-point scale, 1 = never to 5 = always) was adapted for HCPs (modified-PACIC-5A). In both samples, means and standard deviations for each question as well as proportions of responses to each response modality were computed, and an overall score was calculated for the 20-item PACIC. RESULTS: Patients' and HCPs' overall scores were 2.6 (SD 0.9) and 3.6 (SD 0.5), respectively, with HCPs reporting higher scores for all questions except 1. Patients' education and self-management, referral/follow-up and participation in community programs were rated as low by patients and HCPs. CONCLUSION: Healthcare professionals, particularly diabetes specialists, tended to report better PACIC scores than patients, suggesting that care was not reported similarly when received or provided. Evaluation differences might be reduced by a closer collaboration between patients and HCPs, as well as the implementation of community-based interventions considering more patients' perspectives such as patients' education and self-management.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".