Barriers, benefits and interventions for improving the delivery of telemental health services during the coronavirus disease 2019 pandemic: a systematic review
Bibliographic record
Abstract
PURPOSE OF REVIEW: To reduce the spread of infection from the coronavirus disease 2019 (COVID-19), mental healthcare facilities were forced to make the rapid transition from face-to-face services to virtual care. This systematic review aims to synthesize the extant literature reporting on barriers of telemental health (TMH) during the COVID-19 pandemic and how facilities have worked to overcome these barriers, to inform best practices for TMH delivery. RECENT FINDINGS: Most recent findings came from case studies from mental health professionals which reported on barriers related to institutional, provider and patient factors, and how these barriers were overcome. Common barriers identified in the literature include: technological difficulties; issues regarding safety, privacy and confidentiality; therapeutic delivery and the patient-provider relationship; and a loss of sense of community. Studies also reported on the benefits to TMH interventions/tools, as well as suggestions for improvements in the delivery of TMH services. SUMMARY: As the COVID-19 pandemic evolves, mental healthcare providers continue to find creative and feasible solutions to overcome barriers to the delivery of TMH. Dissemination of these solutions is imperative to ensure the best quality of mental healthcare for patients across the globe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".