Medication management for older adults in interprofessional primary care teams: a qualitative interview study of family health teams in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Team-based, interprofessional primary care models are arguably well positioned to care for patients with polypharmacy as they often have a pharmacist or allied health professionals to support patients with medication management. However, little is known about how teams work together to manage medications. This study aimed to explore how a team-based primary care organization including a mix of physicians and interdisciplinary health providers (IHPs), called Family Health Teams (FHTs), manage medications for older adults. METHODS: We conducted semi-structured interviews (n = 38) with administrators, family physicians, and IHPs from six FHTs in Ontario, Canada. We followed the thematic analysis steps outlined by Braun and Clarke and adapted the approach to use a codebook. RESULTS: Four themes were identified: (1) strategic goals and internal policies; (2) tailored programs and supports; (3) diverse team configurations and roles; and (4) teamwork and collaboration. Findings revealed variation in the ways physicians and IHPs worked together to manage medications for older adults and that different approaches to care and physician communication preferences were identified as challenges to medication management. Trust was an important factor in medication management among teams; the more physicians interacted with IHPs, the more comfortable and trusting they were in giving them an active role in patient care. Regardless of the approach to medication management, participants agreed that physicians ultimately had the final say in patient care. CONCLUSIONS: Despite an emphasis on teamwork in FHTs, there were few examples of true collaboration and shared care for medication management. To support older adults and others with complex health needs, opportunities to improve teamwork, strengthen collaboration, and optimize team composition should be identified and pursued.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".