Mental Health Care Guidelines for Telemedicine During the COVID-19 Pandemic: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Mental health care providers have widely adopted telemedicine since the onset of the COVID-19 pandemic. Some providers have reported difficulties in implementing telemedicine and are still assessing its sustainability for their practices. Recommendations, best practices, and guidelines for telemedicine-based mental health care (ie, telemental health care [TMH]) have been published, but the nature and extent of this guidance have not been assessed. OBJECTIVE: We aimed to determine (1) the form of TMH guidelines and recommendations presented to providers, (2) the most commonly presented recommendations and guidelines, and (3) the perceived benefits and challenges of these TMH guidelines and recommendations. METHODS: Through our scoping review of practice guidelines, we aimed to identify themes in TMH guidelines and clinical recommendations published between 2020 and 2024 in peer-reviewed journals. This review focused on the first 2 years of the COVID-19 pandemic to identify and characterize the available TMH guidance. We searched PubMed/MEDLINE and ScienceDirect for articles in peer-reviewed journals published between January 1, 2020, and July 16, 2024. We included articles that were available in English and presented recommendations, best practices, or guidelines for TMH. We excluded duplicates, articles unrelated to telehealth, brief editorial introductions, and those not publicly available. We applied the Healthcare Provider Taxonomy of the National Uniform Claim Committee to article titles and abstracts to identify records relevant to mental health. We used content and thematic analyses to identify key themes. RESULTS: Of the 1348 articles retrieved, we identified 76 that matched our criteria. Through content and thematic analyses, we identified 3 main themes-along with subthemes and topics-related to Facilitators, Concerns, and Changes Advised. The majority of articles called for further research (59/76) and for telemental health education and innovation in some form (43/76) regarding advised changes. Twenty-four articles included specific guidelines, recommendations, or checklists for providers. CONCLUSIONS: The results highlight the need for further large-scale research to support the development of effective guidelines and protocols for therapy plans. Although TMH care is widespread, scholarly work emphasizes the need for a stronger evidence base that includes testing protocols in diverse settings and populations. The results also underscore the importance of increasing health professionals' knowledge of regulatory compliance and providing them with adequate TMH practice education.
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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 | 0.041 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".