Determining the best practices for remote experiential rotations
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, clinical sites have closed their doors to student placements, leading to the implementation of remote rotations. The purpose was to determine best practices for distance preceptorship from the student’s perspective. Methods: A survey was sent to the pharmacy students at the Leslie Dan Faculty of Pharmacy who have completed at least one remote rotation. Results: Forty-eight out of 121 students (39%) completed the survey. It was found that 83% of the students were motivated during the start of their rotations, while 48% remained motivated throughout. Students who remained motivated had clear expectations set from the beginning, felt comfortable communicating issues regarding their assigned work with their preceptor, had similar rapport with remote preceptors as with in-person preceptors, had a preceptor who is always available for questions, and had a work environment free of distractions. Discussion:There are numerous best practices students and preceptors can utilise during a remote rotation to help students remain motivated. Preceptors and students should work together so that students remain motivated throughout their rotation. Setting expectations, having good communication, getting to know their preceptor, and having a work environment free of distractions are key factors for conducting a remote rotation.
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.
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 | 0.034 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".