Pivoting to be Patient-Centered: A Study on Hidden Psychological Costs of Tele-Healthcare and the Patient-Provider Relationship
Bibliographic record
Abstract
Purpose: While the COVID-19 pandemic has necessitated a shift toward virtual medicine, there are some potentially important limitations with this modality of healthcare. One such concern, which has not yet been elucidated, is how telephone-based versus in-person visits differentially impact patients’ perceived autonomy and the resulting patient-provider relationship. Grounded in self-determination theory (SDT), this pilot study addresses this question by investigating the association between patients’ perceived autonomy support and relationship needs satisfaction (autonomy, competence, relatedness) in both types of visits with their family doctor and care team. Methods: Running from Sept. 2020 to Feb. 2021, data was collected via convenience sampling from n = 66 patients (34 in person, 32 virtual) nested within k = 6 family physicians. Patients completed an online survey containing two previously validated scales derived from SDT: The Healthcare Climate Questionnaire and the Basic Need Satisfaction in Relationships Scale. Each scale was adapted to reflect a virtual or in-person visit experience. A random effects model captured the relationship between the motivational variables in each group, adjusting for various sociodemographic effects. Results: Both groups’ perceived autonomy support positively related to their relationship needs satisfaction with their family doctor and care team. Compared to traditional in-person visits, patients perceived the virtual healthcare climate as significantly less autonomy-supportive. Conclusions: In line with SDT, findings from this study suggest that when patients sense a more autonomy-supportive healthcare climate, they will experience a more needs-satisfying relationship with their family doctor and healthcare team. These results have potentially significant implications for supporting patient motivation and facilitating optimal health and wellness outcomes, particularly within the virtual care environment.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".