Young pharmacists as tomorrow’s decision-makers: tool validation and perceptions of pharmaceutical policymaking in Lebanon
Bibliographic record
Abstract
Background: The perception of pharmacy policymaking among early-career pharmacists is crucial for developing and advancing the profession. This study aimed to construct and validate a new tool, the Pharmaceutical Policymaking Perception Scale (PPPS), and assess pharmacy students' and graduates' perceptions of pharmaceutical policymaking in Lebanon. Methods: A standardized questionnaire was disseminated through electronic platforms. It included sociodemographic characteristics, education-related variables, and scales measuring leadership, general self-efficacy, strategic thinking, and public service motivation. The validity of the newly developed PPPS was confirmed, and concepts were linked through multivariate analyses. Results: The PPPS tool exhibited excellent psychometric properties, with its items loading on two factors representing the positive and negative perceptions of pharmaceutical policymaking. The scale demonstrated excellent reliability as well as robust content, construct, structural, and concurrent validity. Only 4% of participants scored above 70, indicating relatively low perceptions of pharmaceutical policymaking in Lebanon. Higher PPPS scores were associated with higher self-efficacy and strategic thinking, while lower scores were linked to reduced public service motivation. No association was found between PPPS and leadership. Conclusion: The novel PPPS scale offers valuable insights into pharmacists' views, enabling a more comprehensive assessment of policymaking perceptions. The potential disconnection between the studied concepts raises concerns. Further research is recommended to confirm these findings, and urgent action by educators and policymakers is essential to effectively engage with early-career pharmacists and enhance their motivation to serve the profession in challenging circumstances.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".