Does one size fit all? A survey of preceptor perceptions and experiences with remote rotations
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: 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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it