Precepting Family Medicine Trainees in Virtual Care: An Exploratory Sequential Mixed Methods Study
Bibliographic record
Abstract
Context: Healthcare systems worldwide embraced virtual care during the COVID-19 pandemic. As the pioneer of telemedicine, Canada has laid the foundations for a remote care revolution on Earth and in Space. However, the precepting roles and experiences to effectively train physicians in virtual healthcare contexts remain understudied. Objective: We aimed to explore the experiences of precepting family medicine residents in virtual healthcare settings. Study Design/Analysis: Informed by social constructivism we conducted a three-phase, sequential, exploratory mixed methods study. Phase I is a scoping literature review on the conceptualization of preceptorship in virtual healthcare settings. Phase II examined the role of clinical preceptors in virtual healthcare settings. To elucidate their experiences, we interviewed preceptors to build a thematic framework about precepting in virtual care (Phase III). We analyzed quantitative survey data using SPSS and qualitative interviews following reflexive thematic analysis. Dataset: We surveyed 45 clinical preceptors of family medicine residents (18% response rate) and analyzed 13 interview transcripts. Population: Eligible physicians have experience as a virtual care provider and clinically affiliation to percept family medicine residents in Canada. Instruments: We piloted and administered a Qualtrics 36-item survey (Nov-Dec 2021) and then used an interview guide to understand the clinical teaching experiences in virtual care (Jan-Mar 2022). Outcomes: We specifically examined the degree of resident exposure, level of preceptor trust, impacts of virtual workplace, nature of precepting, and aspects related to preceptorships in virtual settings. Results: Canadian clinical preceptors reported the nature of their interactions with residents in virtual healthcare settings (e.g., clinical tasks engaged, impacts of the virtual workplace). We described the dynamics of clinical precepting in virtual care contexts according to six key themes: precepting as patchwork, steering away from transactional care, configurations discourage direct supervision, struggling to gauge progress, and centering feedback around clinical story. Conclusion: This article discusses the perceived role and challenges with precepting family medicine residents in virtual healthcare settings, as well as summarizing the pertinent limitations and implications of this research on postgraduate telemedicine training.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".