Specialized competency framework for pharmacists in managerial positions in sales and marketing (SCF-PMSM): development, validation, and correlates
Bibliographic record
Abstract
BACKGROUND: Pharmacists in sales and marketing roles need specific skills for managerial positions, and a framework for evaluating and developing these competencies is necessary. Currently, no such framework is known to exist. OBJECTIVE: This study aimed to develop and validate a specialized competency framework for pharmacists in sales and marketing managerial positions and assess correlates related to the competency domains. METHODS: This web-based study carried out between March and October 2022 enrolled a convenient sample of 60 pharmacists with managerial positions in sales and marketing, contacted by phone, working in the field of sales and marketing from the five governorates of Lebanon (Beirut, Beqaa, Mount Lebanon, South, and North). RESULTS: The framework demonstrated good construct and structural validity in all domains except for emergency preparedness, which had a low correlation with other domains. Competencies were well correlated with respective domains, and behaviors had excellent loadings on corresponding competencies. As for the correlates of the competency domains, males were more confident than females (p < .05), and participants with more experience (or heavier workload) reported higher competency levels (p < .05), particularly for upper management skills, communication skills, and professional practice. Further, education level was not significantly correlated with declared competency, with experience being the most cited source of competence (68.25%), followed by postgraduate degrees (42.48%) and continuing education sessions (33.93%), while undergraduate education was the least reported (29.5%). CONCLUSION: This study could develop and validate the Specialized Competency Framework for Pharmacists in Managerial Positions in Sales and Marketing (SCF-PMSM) among a sample of Lebanese pharmacists. This framework demonstrated good reliability, content, construct, and structural validity in all the domains, with the competencies being well correlated with their respective domains and behaviors having excellent loadings on related competencies, except for emergency preparedness and response. It also revealed a mismatch between what is taught at undergraduate and postgraduate levels and the needs in practice.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".