Problem-Based Learning (PBL) and Public Health
Bibliographic record
Abstract
INTRODUCTION: Worldwide interest in problem-based learning (PBL) has grown in past decades. This article aims to evaluate the perceived effectiveness, appropriateness, benefits, and challenges attributed to the use of PBL in public health education in Vietnam with a view to providing recommendations for curricular design and future policy. METHODS: Teachers at 2 universities in Hanoi participated in group interviews, and students from these 2 universities completed Likert-style questionnaires. RESULTS: Students and teachers regarded PBL positively. However, there was consensus that hybrid models that used PBL alongside other methods are probably the most beneficial for public health education in Vietnam. Teachers discussed the educational and systematic advantages and difficulties associated with PBL. CONCLUSION: Themes arising from this analysis may be helpful in guiding future research-namely, regarding the application of PBL in low- and middle-income countries and in public health. Further exploration of the use of PBL hybrid models is discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".