Videoconferencing-Based Telemental Health: Important Questions for the COVID-19 Era From Clinical and Patient-Centered Perspectives
Bibliographic record
Abstract
The COVID-19 pandemic has intensified the search for digital approaches in mental health treatment, particularly due to patients and clinicians practicing social distancing. This has resulted in the dramatic growth of videoconferencing-based telemental health (V-TMH) services. It is critical for behavioral health providers and those in the mental health field to understand the implications of V-TMH expansion on the stakeholders who use such services, such as patients and clinicians, to provide the service that addresses both patient and clinical needs. Several key questions arise as a result, such as the following: (1) in what ways does V-TMH affect the practice of psychotherapy (ie, clinical needs), (2) to what extent are ethical and patient-centered concerns warranted in terms of V-TMH services (ie, patient needs), and (3) how do factors related to user experience affect treatment dynamics for both the patient and therapist (ie, patient and clinical needs)? We discuss how behavioral health providers can consider the future delivery of mental health care services based on these questions, which pose strong implications for technological innovation, the adaptation of treatments to new technologies, and training professionals in the delivery of V-TMH services and other digital health interventions.
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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 | 0.070 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".