An Interdisciplinary Medication Management Program for Seniors in the Community
Bibliographic record
Abstract
Background: The call for medication management programs (MMPs) is growing, as evidence of inappropriate prescribing and medication use in seniors accumulates. Seniors in social and supportive housing programs are likely at high risk of medication problems by virtue of their socioeconomic status and/or frailty. Yet there are few published reports of MMPs in such settings. Aim: To assess the need for medication management support for seniors in a supportive housing setting; to profile a pharmacist's medication management interventions; and to evaluate the costs, barriers, facilitators, and impact of a MMP. Method: Eleven seniors' apartment buildings in Peel region, served by 1 supportive housing provider, were randomized to experimental or control groups. Seniors in experimental buildings were referred to the MMP by supportive housing supervisors, health care professionals, or themselves. A pharmacist conducted in-home medication reviews. Her drug therapy assessment and recommendations were faxed to the family physician, and shared with other health care providers and caregivers as appropriate. Medication counselling and management aids were provided as needed. On average, clients received 1.7 follow-up visits or phone calls. Medication regimen complexity, adherence, and costs will be compared between groups at 3 months. Additional measures in the experimental group include number of pharmacist-identified medication problems and interventions, acceptance of prescribing recommendations, client and health care provider satisfaction, and service cost. Results: Almost 100 clients have received the service. Of the first 50 clients, 83% were female, with a mean age of 77.7 years and an average of 8 prescription drugs and 2 nonprescription medicines each. The pharmacist identified 2.5 medication problems per client and made 1.7 prescriber interventions and 1.1 client interventions. The most common medication problem (31%) was needing an additional drug and 67% of prescribing recommendations were known to be adopted or accepted. The service required 3.9 hours of pharmacist time per client. Data analysis is in progress. Implications for pharmacists: In-home medication management reviews improve seniors' drug therapy. Opportunities exist for pharmacists to provide this service in nontraditional settings, such as supportive housing or home care programs, thus increasing access and integrating primary care and community care providers in a coordinated medication support system for seniors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".