Medication Management by Family Physicians and Interdisciplinary Health Providers in Ontario Family Health Teams
Bibliographic record
Abstract
Context: Polypharmacy and inappropriate medications can lead to adverse drug events and avoidable health systems costs. Family physicians may find it challenging to manage multiple medications for seniors, especially in fragmented systems with multiple prescribers. Interprofessional primary care teams have capacity for improved medication management. However, we know little about how these teams work together to manage medications. Objective: To describe and understand how family physicians and interdisciplinary health providers (IHPs) work together when managing medications for seniors. Study Design and Analysis: Qualitative semi-structured interviews and thematic analysis. Setting: Interprofessional primary care Family Health Teams (FHTs) in Ontario. Population Studied: Administrators, family physicians, and IHPs (nurses, pharmacists, etc.). Results: Interviews (n=38) were conducted across six FHTs in Ontario. The way physicians and IHPs worked together to manage medications for seniors varied in and across FHTs. We identified three themes in the data related to approach to medication management: 1) no engagement with IHPs (i.e., physicians did not refer their patients to the team’s IHPs), 2) some engagement (i.e., physicians referred patients to IHPs and IHP-led programs for medication management but rarely engaged in ongoing communication), and 3) shared care (i.e., physicians shared decision-making about care with IHPs, there was ongoing communication between physicians and IHPs). Some IHPs were frustrated with tailoring their approach to care and communication based on the preferences of each physician. These differences were perceived to be a result of hierarchy, work style and use of the electronic medical record, and physician expectations. Trust also appeared to be a factor in that the more physicians interacted with IHPs, the more comfortable and trusting they were giving them an active role in patient care. Regardless of the approach to medication management, participants agreed that physicians had the final say in patient care. Conclusions: Despite the FHT model’s emphasis on teamwork, participants did not report a lot of shared care in medication management. While in many cases there was a lack of ongoing communication between IHPs and family physicians, there are opportunities to improve teamwork and strengthen collaboration.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".