Can Source Triangulation Be Used to Overcome Limitations of Self-Assessments? Assessing Educational Needs and Professional Competence of Pharmacists Practicing in Qatar
Bibliographic record
Abstract
INTRODUCTION: Continuing professional development activities should be designed to meet the identified personal goals of the learner. This article aims to explore the self-perceived competency levels and the professional educational needs of pharmacists in Qatar and to compare these with observations of pharmacy students undergoing experiential training in pharmacies (students) and pharmacy academics, directors, and managers (managers). METHODS: Three questionnaires were developed and administered to practicing pharmacists, undergraduate pharmacy students who have performed structured experiential training rotations in multiple pharmacy outlets in Qatar and pharmacy managers. The questionnaires used items extracted from the National Association of Pharmacy Regulatory Authorities (NAPRA) Professional competencies for Canadian pharmacists at entry to practice and measured self- and observed pharmacists' competency and satisfaction with competency level. RESULTS: Training and educational needs were similar between the pharmacists and observers, although there was trend for pharmacists to choose more fact-intensive topics compared with observers whose preferences were toward practice areas. There was no association between the competency level of pharmacists as perceived by observers and as self-assessed by pharmacists (P ≤ .05). Pharmacists' self-assessed competency level was consistently higher than that reported by students (P ≤ .05). DISCUSSION: The results suggest that the use of traditional triangulation might not be sufficient to articulate the professional needs and competencies of practicing pharmacists as part of a strategy to build continuing professional development programs. Pharmacists might have a limited ability to accurately self-assess, and observer assessments might be significantly different from self-assessments which present a dilemma on which assessment to consider closer to reality. The processes currently used to evaluate competence may need to be enhanced through the use of well-designed rubrics or other strategies to empower and to better inform respondents and subsequently improve their ability to self-assess their competencies.
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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 | 0.243 | 0.448 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".