Understanding motivations and behaviours of our influencers: What can pharmacists learn from their leaders?
Bibliographic record
Abstract
Background: There has been considerable discussion regarding the "leadership crisis" in pharmacy, with concerns that insufficient numbers of pharmacists want to take on leadership roles in their own profession. This exploratory study of leaders and influencers in pharmacy was undertaken to characterize the motivations for and behaviours of titled and untitled leaders, in order to help other pharmacists learn from their experiences. Methods: Interviews with 28 individuals who self-identified or were described by others as leaders (with or without formal titles) and influencers in pharmacy were conducted using online platforms (e.g., Zoom, Teams). A semistructured interview guide was used and refined during the interviews. Data were analyzed using a constant comparative method to identify common themes. Results: While participants in this study all described different trajectories towards leadership or influencer roles, several common themes emerged, including 1) personal characteristics that enable leadership roles/activities, 2) environmental supports and drivers that propel leadership forward, 3) positive reinforcers that maintain momentum towards leadership aspirations and 4) general predictors of success as a leader/influencer in pharmacy. Discussion: To address the "leadership crisis" in pharmacy, it will be necessary to motivate and support individuals in assuming these roles. Findings from this study have highlighted the complex and individual pathways current leaders have undertaken to achieve these roles and have signposted ways in which organizations, managers and mentors can support nascent leadership aspirations in productive ways.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".