Virtual Care Use: Profiles of Physician Motivation and Differences in Workplace Need Fulfillment
Bibliographic record
Abstract
Context: Physicians appear to vary in their motivation towards using virtual care, but to what extent remains unclear. According to self-determination theory (SDT), people’s motivation towards tasks or activities can be autonomous, controlled, or both, resulting in groups of individuals with differences in engagement, performance, and wellbeing. A better understanding of physicians’ motivation to use virtual care is therefore needed to help optimize its integration into standard medical practice. Objectives: Guided by SDT, we aimed to determine: if there were indeed distinct groups of physicians based on their motivation towards using virtual care, and if so, how these groups differed in the fulfillment of three basic psychological needs at work – autonomy (i.e., volition), competence (i.e., mastery), and relatedness (i.e., connectedness). Study Design, Setting, & Population Studied: We collected quantitative survey data from a cross-section of 156 family physicians (FPs; 71% female; 61% <50 years) in a large Canadian province. Instrument/Outcome Measures: The online survey included demographic questions and scales for measuring types of motivation towards using virtual care and basic psychological need satisfaction/frustration at work. Analyses: Cluster analysis and analysis of variance were used to explore profiles of physician motivation towards using virtual care and how each profile differed in workplace need fulfillment, respectively. Results: Three higher-order profiles of physician motivation towards using virtual care were identified – ambivalent (66% of FPs), autonomous (19% of FPs), and controlled (16% of FPs) – each of which differed significantly in the degree of need fulfillment at work. Results also revealed that male FPs reported more virtual care use and lower relatedness satisfaction at work, compared to female FPs, and that years in practice was positively associated with autonomous motivation towards using virtual care. Conclusions: This study identifies predominant motivation profiles that FPs currently fit into in using virtual care. Findings suggest that basic psychological needs are fundamental nutrients for FPs to internalize and endorse the value of using virtual care in their practice.
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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 | 0.001 | 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) | 0.002 | 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".