Remote Supervision in Short-Term Global Health Experiences
Bibliographic record
Abstract
The global health development community is increasingly examining the phenomenon of short-term experiences in global health (STEGH), with an aim to mitigate the negative impacts of such activities on host communities. Appropriate supervision is one strategy, but various barriers (e.g., institutional requirements) limit the availability of qualified supervisors. Remote supervision represents one potential model to provide supervision that may mitigate the negative impacts of STEGH. This paper reports observed outcomes from a description of a pilot remote supervision program employed in a global health program for Canadian undergraduate students. Benefits for learners included greater confidence and independence, greater perceived effectiveness in conducting their project abroad, and reassurance of remote support from their supervisor, supplemented with day-to-day guidance from the local partner. Host communities reported greater trust in the bidirectional nature of partnership with the visiting institution, empowerment through directing students' work, and improved alignment of projects with community needs. Finally, faculty noted that remote supervision provided greater flexibility and freedom when compared to traditional in-person supervision, allowing them to maintain professional duties at home. Collectively, this pilot suggests that remote supervision demonstrates a potential solution to mitigating the harms of STEGHs undertaken by learners by providing adequate and appropriate remote supervision.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".