Investigating Patient Uncertainty in Virtual Consultation: A Content Analysis Study
Bibliographic record
Abstract
Despite the benefits of conveniences and flexibilities, widespread adoption of virtual consultation systems remains elusive among patients. This exploratory study aims to uncover the underlying reasons behind this phenomenon by investigating patient uncertainties about virtual consultation and their impact on patient satisfaction. Leveraging a content analysis methodology, we scrutinize patient-generated online reviews on five prominent virtual consultation platforms. Our findings delineate the primary sources of patient uncertainty, ranked in descending order of significance: (1) ambiguity surrounding virtual consultation processes, (2) concerns regarding doctors' behavior, (3) challenges in articulating symptoms, (4) apprehensions regarding doctors' attitudes, (5) difficulties in understanding doctors, and (6) uncertainty about doctor’s feelings and emotions. Moreover, our analysis suggests a potential link between heightened uncertainties and patient dissatisfaction, albeit contingent on various factors, including perceived benefits and how virtual consultation systems address patient uncertainties. This study sheds light on the intricate dynamics of uncertainties in virtual consultations, providing valuable insights for researchers, system designers, and healthcare providers. By elucidating patient perspectives and apprehensions, our findings offer a roadmap for refining virtual consultation systems to mitigate uncertainties and enhance patient satisfaction, thereby advancing the quality and efficacy of telemedicine services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".