Placements in global health masters' programmes: what is the student experience?
Bibliographic record
Abstract
BACKGROUND: Global health training is increasingly part of public health training in high-income countries, with placements as key components. We sought preliminary evidence of student placement experiences and learning through masters' programmes at the universities of Oxford and Toronto. METHODS: In a mixed-methods design, we drew on existing programme records, student feedback surveys (Oxford only) and semi-structured interviews with graduates. RESULTS: Students participated in practice, informed policy and conducted research across a wide variety of topics, and with a range of different tasks, mostly overseas. Building on existing collaboration- or partnership-facilitated placement setup. Clear communication and face-to-face time with organizational representatives or on-site supervisors helped clarify placement objectives. Flexibility on students' and supervisors' part enabled students to take advantage of urgent public health activities for learning. Students valued the opportunity to make cross-country comparisons, to see first-hand the role of international organizations and to learn concrete skills in project design, questionnaire formulation, qualitative and quantitative analysis and writing up. CONCLUSIONS: Placements were valuable to public health professionals in training. We encourage other programmes to share placement experience of their students, field supervisors and host organizations.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".