Determinants of Patient Use of Telemental Health Services: Representative Cross-Sectional Survey From Germany
Bibliographic record
Abstract
Background: Telemental health services effectively address major challenges in mental health care delivery. To maximize the potential of the services, it is essential to facilitate patient use and reduce use disparities. Nevertheless, determinants of patient use of telemental health services have been scarcely investigated thus far. Objective: We aimed to identify determinants of patient use of telemental health services since the onset of the COVID-19 pandemic and in the last 4 weeks. Methods: In December 2023, we conducted a cross-sectional, quota-based (gender and age group) online survey. The sample comprised individuals aged 18 to 74 years, who had been using mental health services since March 2020 (n=2082). Telemental health service use was assessed using items that inquired whether individuals had used the services since March 2020 or currently (in the last 4 weeks). Logistic regressions were computed to test the associations of socioeconomic, access, health, COVID-19-related, psychosocial, and service factors, as well as personality and provider characteristics with patient use. Results: Younger age, a more positive patient attitude toward telemental health services, a more positive provider attitude toward using the services, and higher provider skills for using the services were positively associated with patient use of telemental health services since the onset of the COVID-19 pandemic. When exclusively looking at current use, positive associations with full-time employment, lower neuroticism, a more positive provider attitude toward the services, and use of the services to avoid stigmatization, long waiting times, or inconvenient scheduling were observed. Access, health, and COVID-19-related factors were not associated with patient use (since the onset of the COVID-19 pandemic and currently). Conclusions: Beyond socioeconomic factors, personality, and a positive patient attitude toward the services, patient use of telemental health services was associated with a positive provider attitude toward using the services and higher provider skills for using the services, which underscores the need for provider support and training in telemental health care. Furthermore, avoiding stigmatization and higher convenience of the services were associated with patient use, which highlights the substantial potential of the services to address current mental health care challenges.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".