A Goffmanian analysis of impact of unclear professional identity and role negotiation of pharmacists in primary care: A multiple case study
Bibliographic record
Abstract
BACKGROUND: Professional identity and its development is a focus of research, education, and practice. But, there is a lack of how professional identity impacts changes in pharmacists' roles in practice, which are particularly prevalent in primary care teams. OBJECTIVES: This research uses Goffmanian theory, micro-sociologic interactional theory, to describe the outcomes of role negotiation in integrated primary care teams. METHODS: This is a multiple case study done per Yin, which used interviews and documents to collect data. Interviews used a storytelling format to gather information on the pharmacist's role and negotiation with their team. Four to six interviews were done in each case. Data was analyzed in an iterative manner using the Qualitative approach by Leuven including narrative reports being created for each case. RESULTS: Five cases were recruited but three cases were completed. In each case, the pharmacist was passive in role negotiation and allowed other actors to decide what tasks were of value. Likely this passivity was due to their professional identities: supportive and "not a physician". These identities led to a focus on the pharmacists' need to develop. This multi-case study demonstrated that pharmacists' professional identity led to passivity being valued and expected. Whether pharmacists self-limited, which has been previously seen, needs to be better defined. But unclear archetypes reduced tasks identified as unique to the pharmacist. CONCLUSION: Goffmanian theory highlighted a key success for future pharmacist role negotiation, a clear professional identity by both pharmacists and society, including team members. Until that occurs, there is a risk of underuse in primary care team settings.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".