Exploring the Preferences and Behavioral Trends of e-Patients in Psychosomatics Towards Telemedicine During and Post COVID-19 Pandemic: Cross-Sectional Analysis
Bibliographic record
Abstract
Background: COVID-19 accelerated the adoption of health services, with a growing number of psychosomatic patients turning to the internet for health-related decisions. This study explored changes in communication behavior, information-seeking habits, and post-pandemic consultation preferences among psychosomatic patients during and after COVID-19. Objective: This study explored changes in communication behavior, information-seeking habits, and post-pandemic consultation preferences among psychosomatic patients during and after COVID-19. Methods: In a cross-sectional study, 150 adult patients (>18 y) from the psychosomatic outpatient department in Tübingen, Germany, were invited to complete an ad hoc questionnaire to identify e-patients' preferences related to communication, information-seeking behavior, subjective explanations, and postpandemic preferences. Group comparisons and multiple linear regression analyses were conducted. Results: The study revealed a slight increase in online-based communication between patients and caregivers (eg, caregivers' system use +10.8%; live video consultations +30.8%), as well as in patient-patient interactions (eg, online correspondence +16.9%). Significant group differences were observed for social media correspondence by patient age (χ2¬=17.44, P<.001) and for live video consultations by gender (χ2¬=70.17, P<.001). For both age and gender, significant group differences were found in the use of medical videos (age χ2¬=6.36, P=.04; gender χ2¬=76.70, P<.001). Age, gender, and preferences for live video consultations were identified as significant predictors (F1,11=14.195, P<.001, R²=0.299) of patients' future preferences for on-site versus online consultations. Conclusions: Although a slight increase in online-based communication was observed, resilience in patient-caregiver and patient-patient communication indicated relative stability under challenging circumstances. Older adults and female patients expressed a preference for on-site consultations, emphasizing the significance of in-person care. Male patients demonstrated greater openness towards online consultations, indicating potential for expansion of eHealth services. Identifying preferences is a core essential for providing future eHealth implementations that account for diverse patient needs and for designing care offerings. Incorporating these findings may enhance patient engagement and satisfaction in the evolving landscape of eHealth services for better health care outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".