Research self-efficacy in early-career pharmacists: Tool validation and correlation with personal attributes
Bibliographic record
Abstract
Introduction: Self-efficacy in research and personal characteristics of pharmacists are necessary to lead and implement pharmaceutical research strategies. This study primarily aimed to confirm the validity of the Research Self-Efficacy Scale (RSES) among early-career pharmacists; a secondary objective was to assess participants' perspectives on research self-efficacy while considering personal characteristics, such as strategic thinking and leadership. Methods: Using an exploratory factor analysis and internal consistency measure, the RSES scale validity and reliability were assessed among Lebanese early-career pharmacists. Its association with personal attributes, such as global self-efficacy, leadership, and strategic thinking, was also assessed through correlation with validated measures. Results: The RSES scale was found to be valid and reliable. Pharmacists from foreign universities scored higher on the RSES compared to their counterparts from Lebanese institutions. Additionally, a positive correlation was found between self-efficacy, generalized self-efficacy, and strategic thinking scores, while differences between universities and year of study did not reach statistical significance. Conclusion: This study demonstrated that the RSES is a valid and reliable tool for assessing research self-efficacy among early-career pharmacists in Lebanon. Several strategies could be implemented at the institutional and national levels to strengthen self-efficacy and cultivate a sustainable research environment. These include enhancing educational frameworks, integrating research opportunities, and fostering international collaborations. Future research using this validated scale will be instrumental in evaluating the effectiveness of such interventions.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".