Exploring the impact of pharmacist comprehensive annual care plans on perceived quality of chronic illness care by patients in Alberta, Canada
Bibliographic record
Abstract
Background:In 2012, the Government of Alberta introduced a funding program to remunerate pharmacists to develop a comprehensive annual care plan (CACP) for patients with complex needs. The objective of this study is to explore patients’ perceptions of the care they received through the pharmacist CACP program in Alberta.Methods:We invited 3442 patients who received a pharmacist-billed CACP within the previous 3 months and 6888 matched controls across Alberta to complete an online questionnaire. The questionnaire consisted of the short version Patient Assessment of Chronic Illness Care (PACIC-11), with 3 additional pharmacy-specific assessment questions added. Additional questions related to health status and demographics were also included.Results:Overall, most patients indicated a low level of chronic illness care by pharmacists, with few differences noted between CACP patients and non-CACP controls. Of note, controls reported higher quality of care for 5 domains within the adapted PACIC-like tool compared with CACP patients (p < 0.05 for all). Interestingly, only 79 (44%) of CACP patients reported that they had received a CACP, whereas only 192 (66%) of control patients reported that they did not receive a care plan. In a sensitivity analysis including only these respondents, individuals who received a CACP perceived a significantly higher quality of chronic illness care across all PACIC domains.Conclusion:Overall, chronic illness care incentivized by the pharmacist CACP program in Alberta is perceived to be moderate to low. When limited to respondents who explicitly recognized receiving the service or not, the perceptions of quality of care were more positive. This suggests that better implementation of CACP by pharmacists may be associated with improved quality of care and that some redesign is needed to engage patients more. Can Pharm J (Ott) 2021;154:xx-xx.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".