Community pharmacists’ attitudes, opinions and beliefs about leadership in the profession: An exploratory study
Bibliographic record
Abstract
BACKGROUND: The profession of pharmacy needs effective leaders to navigate change. Indirect indicators suggest there are insufficient numbers of pharmacists who actually want to be leaders. A paucity of research limits our understanding of what motivates and demotivates pharmacists to be leaders. This exploratory study was undertaken to investigate community pharmacists' attitudes, opinions and beliefs about leadership. METHODS: Interviews with 38 pharmacists were conducted either in person or using telecommunication applications such as Skype. A semistructured interview guide was used to elicit comments about leadership in general and in pharmacy, perceived leadership roles and barriers/enablers to leadership. Data were analyzed using Chan and Drasgow's motivation-to-lead framework. RESULTS: Key barriers to assuming leadership roles included lack of education/support, inadequate compensation, concerns about work-life balance, time constraints and a generalized discontent about leadership in society and in the profession. DISCUSSION: While some of these barriers could be addressed through formal education (such as conflict management training) or through managerial influence (e.g., remuneration or scheduling to improve work-life balance), some (such as cynicism about leadership) will be more challenging to address. The need to address these barriers will grow as the need for new and emerging leaders in pharmacy continues to evolve.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".