Precepting at the time of a natural disaster
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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 it