How the Role of the Pharmacist is Negotiated in Ontario Family Health Teams: A Multiple Case Study
Bibliographic record
Abstract
Including pharmacists on interprofessional primary care teams has demonstrated improvements in patient safety and clinical outcomes but there are concerns about under-utilization. Using Goffman’s micro-sociological theories of self and impression management, this dissertation explored the negotiation of the pharmacists’ role in Ontario Family Health Teams (FHTs). This dissertation used a multiple case study per Yin. Recruitment of five cases was used to ensure cross-case analysis. Cases were varied on geography, FHT size, and team tenure. Data was collected using both semi-structured interviews and document analysis. At least four participants were interviewed in each case. All data was stored in MAXQDA. Thematic analysis was completed using the Quality Analysis of Leuven (QUAGOL) framework. Positionality was completed through reflection and assisted by Social Identity Map. Three cases were recruited and analyzed. Case A demonstrated the organization led the negotiation of the pharmacist’s role, Case B demonstrated that physicians led, and Case C demonstrated a lack of active negotiation of the pharmacist’s role. The cross-case analysis highlighted pharmacists were not actively involved in negotiating their role due to their professional identities. This led to uncertainties of who should lead role negotiations. Organizations with more formal procedures tended to have proactive pharmacist’s roles including independent access to patients. The pharmacists’ supportive identity and ongoing comparison to physicians may have resulted in their lack of archetype. This contributed to unclear goals for role negotiation. Additionally, this contributed to physicians being the audience for the pharmacist’s role and a focus on tasks and delivery that physicians valued. Most patient-care decisions were referred back to physicians. The lack of focus on patients’ experiences and outcomes was likely an important aspect of role negotiation. Stakeholders such as patients, pharmacists, their institutions, and organizations, can use these results to negotiate roles efficiently. Organizations could use programming to assign new tasks to the pharmacist for successful implementation. Future research should look at the pharmacist’s professional identity and its relationship to patient-centered care.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".