Pharmacist's contribution in a heart function clinic: patient perception and medication appropriateness.
Bibliographic record
Abstract
BACKGROUND: It has been cited that the management of congestive heart failure (CHF) requires a multidisciplinary approach; however, the role of the pharmacist has not been extensively studied. The roles for pharmacists are changing to meet the long term needs of patients in the community setting, including patients with CHF. OBJECTIVES: To evaluate the effect of a pharmacist on the appropriateness of medications taken by patients in the heart function clinic using the Medication Appropriateness Index (MAI) and to measure the effect of a pharmacist on the patients' response to the pharmacist's interventions using the Purdue Directive Guidance (DG) scale. METHODS: Eighty patients attending the heart function clinic at The University Health Network, Toronto General Hospital Toronto, Ontario were randomly assigned to an intervention group that received pharmacist services or a nonintervention group that received usual care from the clinic staff. Patients were assessed at baseline and at one-month follow-up. RESULTS: The change in MAI score from baseline was 0.74 and 0.49 for the intervention and nonintervention groups, respectively (P=0.605). The change in DG survey results was 9.97 and 1.00 for the intervention and nonintervention groups, respectively (P<0.001). The intervention group improved significantly in all components of the DG survey, especially those pertaining to feedback and goal setting. CONCLUSIONS: A benefit was demonstrated for 'directive guidance' of patients, in the form of education and goal setting as shown by positive survey results.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".