Patient Preference for Telehealth Background Shapes Impressions of Physicians and Information Recall: A Randomized Experiment
Bibliographic record
Abstract
Introduction: Telehealth is increasing rapidly as a health care delivery platform, but we lack empirical evidence regarding how telehealth environments can affect patient experiences. The present research determined how physician's telehealth backgrounds affect various patient outcomes. Methods: Participants viewed a 30-s video of a physician with one of six different virtual backgrounds and reported various socioemotional and cognitive responses to the mock telehealth experience. Results: Although the telehealth background manipulation did not impact participants' socioemotional or cognitive responses, participants' subjective perceptions of the telehealth backgrounds were related to important clinical outcomes, such as their ability to remember critical information from the appointment and overall satisfaction with the experience. Discussion: Telehealth environments may result in tradeoffs between patient experience, subjective impressions of clinicians, and information recall. Conclusions: A physician's telehealth background can have measurable impact on patients' telehealth experiences, suggesting a need for careful background selection and design.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".