Transitioning to Telehealth: The Multifaceted Impact of a Midcareer Transition in Practice
Bibliographic record
Abstract
PURPOSE: The medical education continuum is interrupted by several transition periods that can adversely affect performance. Most of what has been learned about such periods focuses upon movement from one stage of training to another and movement from training to practice. Established physicians, however, experience transitions throughout their careers at idiosyncratic times and with little assistance. Better understanding how physicians experience transition, where they struggle and how they adapt, would enable better support to be provided. We investigated the COVID-19-forced transition in clinical practice to virtual care, particularly its effect on physician roles and the ways that established physicians faced challenges they encountered when transitioning to virtual care. METHOD: Ten semistructured interviews were conducted between November 2020 and February 2021 with physicians across different specialties and practice contexts who transitioned their practice to virtual care during the COVID-19 pandemic. Interview data were analyzed iteratively using "generic qualitative methodology" with constant comparison to identify themes in relation to observations. RESULTS: The transition to telehealth had implications that extended beyond the patient encounter, appearing to affect all aspects of the physician's practice. To reflect that, CanMEDS was chosen as a useful organizing framework. The effects, captured in the theme "changes to the physician's roles," were nuanced, illustrated a consistent need to adapt to context, and could be framed positively or negatively or both. Additionally identified themes were labeled "physicians' mental health" and "strategies to mitigate challenges." These themes highlighted that, despite the effort involved and novelty of the situation, all participants found remarkably similar ways of grappling with the challenges faced. CONCLUSIONS: While the basic roles of the physician do not appear to have changed through the transition to telehealth, our findings indicated that these roles were redefined in fundamental ways in response to changing societal needs.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".