Determinants of Patient Satisfaction With Telemental Health Services in Germany: Representative Cross-Sectional Postpandemic Survey Study
Bibliographic record
Abstract
Background: Increasing patient satisfaction with telemental health services is crucial for promoting widespread implementation and ensuring consistent utilization rates in the future, where these services could be a beneficial addition to routine mental health care. Nevertheless, knowledge regarding determinants of patient satisfaction with telemental health services is very limited. Objective: This study aimed to identify determinants of patient satisfaction with telemental health services. Methods: A cross-sectional, quota-based (quotas: gender and age group), web-based survey was conducted in December 2023 in Germany. The sample included individuals aged 18-74 years who had received telemental health services since March 2020 (N=854). Patient satisfaction with video, telephone, and asynchronous services was measured using the Telemedicine Satisfaction Questionnaire or the Client Satisfaction Questionnaire adapted to internet-based interventions. The association of socioeconomic, access, health, psychosocial, personality, and COVID-19-related factors, as well as patient preferences and provider characteristics with patient satisfaction, was tested using multiple linear regressions. Results: A positive patient attitude towards telemental health services and greater fear of COVID-19 as well as a positive and open provider attitude and higher provider skills for using the services were positively associated with patient satisfaction in all service groups (P<.05). Furthermore, the patients' educational level, employment status, relationship status, certain personality factors, technology commitment, loneliness, self-efficacy, and internet connection quality at home were significantly associated with satisfaction in at least 1 service group. Physical and mental health determinants were not significantly associated with the outcome. Conclusions: Satisfaction with telemental health services is particularly associated with psychosocial characteristics, individual preferences, and attitudes of patients, which should be considered when addressing target groups for the services. Furthermore, positive provider attitudes towards and higher skills for using the services are heavily associated with patient satisfaction. Training and support for providers should be prioritized to promote patient satisfaction and widespread use of future services.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".