Precepting at the time of a natural disaster
Bibliographic record
Abstract
BACKGROUND: Natural disasters strike communities that have varied degrees of preparedness, both physical and psychological. Rural communities may be particularly vulnerable as they often do not have the infrastructure or resources to prepare in advance. The psychological impact of a natural disaster is amplified in learners who may be temporary members of the community and therefore cannot draw on personal support during the crisis. They may turn to their clinical preceptors for guidance. CONTEXT: The Slave Lake fire (population 6782) in May 2011 and the High River flood (population 12 920) in June 2013 are examples of natural disasters that have occurred in rural Alberta, Canada. At the time of these critical incidents, three medical students and one family medicine resident from the two provincial medical schools were participating in rotations in these communities. INNOVATION: Although disasters occur rarely, there is a need for guidelines for preceptors from the learner perspective. Accordingly, using a modified Delphi approach, we captured the experiences of learners that were then refined into two themes, each containing three recommendations: considerations for action during a natural disaster and considerations for action after the acute crisis has passed. Although disasters occur rarely, there is a need for guidelines for preceptors from the learner perspective IMPLICATIONS: Our recommendations provide suggestions for practical solutions that build on the usual expectations of mentors and may benefit the student-teacher relationship at the time of a disaster and beyond. They are meant to initiate discussion regarding further study aimed towards creating recommendations for preceptor response that may cross disciplines.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".