Does one size fit all? A survey of preceptor perceptions and experiences with remote rotations
Bibliographic record
Abstract
Background: During the pandemic, experiential rotations transitioned from in-person to remote rotations. Methods: The authors surveyed preceptors about their experiences and perceptions of remote rotations. Preceptors completed an online questionnaire divided into six domains: 1) General demographics; 2) Preceptor/student relationship; 3) Preceptor support and continuing professional development opportunities; 4) Technology; 5) Preceptor perceptions; and 6) Motivators and challenges. Responses were coded and analysed for emerging themes. Results: A total of 47 out of 157 preceptors (30%) responded to the questionnaire, and most preceptors were willing to precept remotely again (85%). Student responsiveness (87%) and enjoyment of teaching (83%) were the greatest motivators. Major themes reflected the preceptor’s struggles in building rapport and facilitating in-the-moment learning opportunities. Preceptors identified guidance and on-going support as key factors to ensure preceptor and student readiness and to manage expectations. The formula for a successful rotation included careful consideration of appropriate pedagogy, technology, and a dose of motivation. Conclusion: Preceptors reflected a positive experience in leading remote rotations. Traditional precepting approaches employed during in-person rotations need to be adapted and individualised for the context of remote rotations, highlighting that there is no ‘one-size-fits-all’ approach. Transitioning to a remote environment generates new opportunities and drives innovation.
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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 | 0.005 | 0.018 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".