Does a blended learning environment suit advanced practice training for pharmacists in a Middle East setting?
Bibliographic record
Abstract
OBJECTIVES: The transfer of pedagogies and instructional techniques outside their contexts of origin may not be always be suitable for intended learners. The aim of this study was to explore the experiences of Middle East pharmacists enrolled in advanced pharmacy practice courses delivered through a blended learning environment (BLE). METHODS: Seventeen students and graduates from a BLE in Qatar participated in focus group interviews. A topic guide was developed to elicit these pharmacists' perspectives on perceived barriers to completing the courses and facilitating factors for content engagement and overall satisfaction. Discussions were recorded, transcribed verbatim and text analysed using thematic content analysis. KEY FINDINGS: We identified three predominant themes in our analysis of these discussions: (1) relevance, (2) motivation and (3) communication. Participants favourably endorsed any programme aspect that linked with their workplace care responsibilities, but found it challenging to adapt to high-fidelity testing environments. The on-campus sessions were key for sustaining motivation and recommitting to time management and organisation with the distance-based content. Although these students expressed difficulty in understanding posted assignment instructions and feedback and occasionally faced technological issues, they were overwhelmingly satisfied with how the programme contributed to advancing their practice capabilities. CONCLUSIONS: Pharmacists enrolled in BLE advanced pharmacy practice courses in Qatar identified barriers and facilitators like those experienced by professional learners elsewhere. However, we found that instructional design and communication approaches merit some special consideration for Arab students for optimal engagement in BLE.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".