49 Simulation Preparation for Experiences in Resource Limited Settings (SimPERLS): An innovative pre-departure training program for pediatric healthcare professionals
Bibliographic record
Abstract
Pre-departure training (P-DT) for experiences in resource limited settings is the expected standard for healthcare professionals in 2018. Traditional P-DT has focused on health and safety as well as medical expert skills. However, there has been recent focus shift to deal with the emotional as well as medical challenges. The study purpose was to pilot a multidisciplinary, simulation based P-DT program for health professionals preparing for experiences both within and outside of Canada. We piloted four simulation-based scenarios designed to challenge clinical skills as well as evoke emotions that may be experienced in resource limited settings. Two scenarios were set in a global health context and two scenarios were adapted for Canadian locales. Workshop was held over one afternoon and each scenario was allocated one hour total (inclusive of simulation and debriefing). Participants experienced each scenario and were debriefed thereafter as per standard simulation protocol. An evaluation questionnaire (including both likert scale and open-ended, short-answer style) was completed at the end of the course. There were a total of fourteen participants from various health professions. Overall, the course received an average 4.4/5 score from participants. Most participants said they gained new insight and learned new applicable skills with an average score of 4.8/5 and 4.7/5 respectively. Common emotions reported during the simulations included: sadness, helplessness, and frustration. Participants were motivated to prepare for upcoming experiences by increasing their core medical knowledge, utilizing the WHO handbook and increasing cultural awareness. Participants felt encouraged to seek opportunities for work in resource limited settings. Next steps include follow up with participants after their low resource setting experiences to evaluate how this training impacts their preparation and overall journey.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".