Assessment of the implementation of problem-based learning model in Saudi medical colleges: a cross-sectional study
Bibliographic record
Abstract
Hassan Al-shehri,1 Mohamed O’haj,2 Rayan Elsini,3 Hatem Alharbi,3 Mosleh Jabari,1 Fahad Al-Qashar,4 Niels Rochow5 1Department of Pediatrics, College of Medicine, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia; 2Department of Biochemistry, College of Medicine, University of Bisha, Bisha, Saudi Arabia; 3College of Medicine, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia; 4Department of Pediatrics, Bahrain Defence Force Hospital, Royal Medical Services, Riffa, Bahrain; 5Division of Neonatology, Department of Pediatrics, McMaster University, Hamilton, ON, Canada Background: Problem-based learning (PBL) is a method by which students solve clinical scenarios in a small group discussion. The aim of this study was to assess the implementation of PBL in Saudi Universities.Methods: This is a cross-sectional study including 151 participants from 16 universities. A questionnaire was distributed to the faculty members through e-mail messages. The questionnaire consisted of 35 questions with 5-point Likert scale arranged in three subscales.Results: The total mean of PBL implementation score was 2.5 (SD =0.39). The scores of the three PBL implementation subscales showed marked variance, with the average score of the subscale “overall PBL experience in my college” being the most highly affected, with an average score of (3.07, SD =0.72), followed by “implementation of PBL model” (2.36, SD=0.47). The least affected subscale was “preparation for PBL implementation” (2.13, SD =0.67).Conclusion: Relatively moderate level of PBL implementation was observed in Saudi Arabia. However, we suggest that more courses should be introduced in order to improve the skills of faculty members and provide a strong infrastructure to implement PBL model in Saudi medical colleges. Keywords: Saudi Arabia, medical students, problem-based learning, implementation
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".