Pre-departure and Post-elective Requirements for Global Health Electives: Survey of Canadian Royal College Emergency Medicine Programs
Bibliographic record
Abstract
OBJECTIVES: Global Health (GH) electives offer unique learning opportunities; however, risks to trainees and host populations should be minimized through pre-departure training and post-elective debriefing. In a 2016 study, only three Canadian residency programs mandated such training, although specific data on Emergency Medicine (EM) programs is lacking. This study aimed to identify GH elective requirements and perceived training gaps among EM programs. METHODS: We conducted two email surveys (one each for EM program directors [PDs] and residents) regarding training requirements and perceived gaps for GH electives. We also contacted university postgraduate medical education (PGME) and GH offices, via their online publicized emails, to assess university-wide requirements and resources. RESULTS: Nine PDs responded, with 78% reporting having residents participate in GH electives. Many PDs (67%) believed residents were moderately prepared for GH electives, while 33% felt they were unprepared to some degree. Forty seven out of an estimated 380 EM residents responded with 35% having completed a GH elective during residency. Of those, only one (6%) reported feeling very prepared, and 43% believed there was a need to improve trainings. Uncertainty around training requirements was reported, and residents identified challenges faced on electives, as well as priority topics for training. Responses from PGME and GH offices indicated that pre-departure training and post-elective debriefing were required or available at more universities than was indicated by the PD and resident respondents. However university requirements varied widely, with some exclusively requiring basic travel information and Health and Safety checklists or modules. The disparate responses indicate that residents and PDs may either be unaware of university requirements or not utilize available training resources for GH electives. CONCLUSIONS: Although Canadian EM residents participate in GH electives, the majority of training programs do not require pre-departure training or post-elective debriefing. PDs and residents report varying levels of preparedness, and residents acknowledge a variety of challenges during GH electives. This information can be used to inform pre-departure training and post-elective debriefing and encourage EM residents to access available university-wide training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".