Bibliographic record
Abstract
BACKGROUND: Intraprofessional conflict among pharmacists, regulated technicians and assistants may undermine attempts to advance patient care in community pharmacy. There is no available research examining this issue in light of the evolution of the profession and roles within the profession. METHODS: A combination of interviews and focus groups involving pharmacists, technicians and assistants was undertaken. Each participant completed the Conflict Management Scale as a way of identifying conflict management style. Data were analyzed and coded using a constant-comparative, iterative method. RESULTS: A total of 41 pharmacy team members participated in this research (14 pharmacists, 14 technicians and 13 assistants). Four key themes were identified that related to conflict within community pharmacy: role misunderstanding, threats to self-identity, differences in conflict management style and workplace demotivation. INTERPRETATION: As exploratory research, this study highlighted the need for greater role clarity and additional conflict management skills training as supports for the pharmacy team. The impact of conflict in the workplace was described by participants as significant, adverse and multifactorial. CONCLUSIONS: To support practice change, there has been major evolution of roles and responsibilities of pharmacists, technicians and assistants. Conflict among pharmacy team members has the potential to adversely affect the quality of care provided to patients and is an issue for managers, owners, regulators and educators.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".