Profiles of physician motivation towards using virtual care: differences in workplace need fulfillment
Bibliographic record
Abstract
BACKGROUND: Physicians appear to vary in their motivation towards using virtual care, but to what extent is unclear. To better understand this variance, which is important for supporting physician wellbeing and therefore patient care, the authors used self-determination theory's (SDT) framework. According to SDT, different types of motivation exist, ranging from controlled to autonomous, that lend to differences in engagement, performance, and wellbeing. The authors aimed to determine: (a) if there were distinct groups of physicians based on their quality of motivation towards using virtual care, and if so, (b) how these groups varied in fulfillment of basic psychological needs (autonomy, competence, and relatedness) in the workplace. METHODS: In March-August 2022, the authors collected quantitative, survey-based data from a cross-section of 156 family physicians in Alberta, Canada. The survey contained existing scales that measure types of motivation (autonomous vs. controlled) and basic psychological need satisfaction/frustration at work. Cluster analysis was used to explore profiles of physician motivation towards using virtual care, and analysis of variance was used to determine how each profile differed with respect to workplace need fulfillment. RESULTS: With motivation towards using virtual care, three higher-order profiles of physician motivation were identified: autonomous (19% family physicians), controlled (16% of family physicians), and ambivalent (66% of family physicians). The three profiles differed significantly in terms of psychological need fulfillment at work. CONCLUSIONS: This study identifies specific profiles that family physicians currently fall into when it comes to motivation towards using virtual care. In line with SDT, findings suggest that basic psychological needs are fundamental nutrients for physicians to internalize and endorse the value of using virtual care in their practices. Implications for physician wellbeing are discussed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".