Family Physician Motivation and Well-Being in the Digital Era
Bibliographic record
Abstract
PURPOSE: Family physicians rapidly shifted to using virtual care during the COVID-19 pandemic, yet it is largely unknown if this change has impacted their workplace motivation. A better understanding of this matter is essential for optimizing the integration of virtual care into standard practice and for supporting family physician well-being. Using a self-determination theory lens, we examined if family physicians experienced autonomous (vs controlled) motivation toward using virtual care, how this related to their subjective well-being, and whether satisfaction (vs frustration) of their basic psychological needs at work mediated that relationship. METHODS: Using cross-sectional survey methodology, quantitative data was collected from 156 family physicians in Alberta, Canada. The questionnaire contained validated scales for measuring motivational quality, workplace need fulfillment, and subjective well-being. Descriptive, correlational, and mediation analyses were performed. RESULTS: Family physicians varied significantly in their quality of motivation towards using virtual care. Controlled motivation toward using virtual care was associated with lower well-being, and workplace need frustration fully mediated that relationship. Conversely, workplace need satisfaction, but not autonomous motivation toward using virtual care, was associated with higher well-being. CONCLUSIONS: In line with self-determination theory, findings suggest that when family physicians' motivation toward using virtual care is less self-determined, it will lead to poorer subjective well-being, because of basic psychological need frustration. Potential implications of the findings are discussed within the contexts of virtual health and primary care.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".