Virtual Care Integration: Balancing Physician Well-Being
Bibliographic record
Abstract
Background and Objectives: According to self-determination theory (SDT), fulfillment of three basic psychological needs-autonomy, competence, and relatedness-positively impacts people's health and well-being. Amid the COVID-19 pandemic, an accelerated adoption of virtual care practices coincided with a decline in the well-being of physicians. Taking into account the frequency of virtual care use, we examined the relationship between workplace need fulfillment and physician well-being. Methods: Using online survey methodology, in March through June 2022, we collected data from 156 family physicians (FPs) in Alberta, Canada. The survey contained scales that measured workplace need satisfaction and frustration, subjective well-being (physical, psychological, and relational), and frequency of virtual care use. We performed correlational and regression analyses of the data. Results: More frequent use of virtual care was associated with lower relatedness satisfaction among FPs. Controlling for the frequency of virtual care use, frustration of autonomy and competence needs negatively related to FPs' physical well-being; frustration of competence and relatedness needs negatively related to their psychological and relational well-being. Conclusions: Findings from this study align with SDT and underscore the importance of supporting FPs' basic psychological needs, while we work to integrate virtual care into clinical practice. In their day-to-day work, we encourage physicians to reflect on their own sense of autonomy, competence, and relatedness, and consider how using virtual care aligns with these basic 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".