Synchronous Web-Based Psychotherapy for Mental Disorders From a Health Quality Perspective: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic necessitated rapid changes to health care delivery, including a shift from in-person to digitally delivered psychotherapy. While these changes helped ensure timely psychotherapy provision, many concerns exist, including clinical, cultural, practical, privacy, and security issues. OBJECTIVE: This scoping review systematically mapped existing peer-reviewed research on synchronous, therapist-delivered web-based psychotherapy for individuals with a diagnosed mental illness. Data were analyzed through the lens of the Alberta Quality Matrix for Health (AQMH) to assess to what degree this literature addresses key indicators of health care quality. This analysis aided in the identification and organization of knowledge gaps with regard to web-based psychotherapies, highlighting potential disparities between previously prioritized dimensions of care and those requiring further attention. METHODS: This review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We included peer-reviewed primary research studies in the English language investigating synchronous, therapist-delivered remote psychotherapy delivered to adults (aged 18 years and older) with a Diagnostic and Statistical Manual of Mental Disorders or International Statistical Classification of Diseases diagnosed mental illness. All other citations were excluded. Relevant studies were identified through MEDLINE, APA PsycINFO, Embase (OVID), Web of Science: Core Collection (Clarivate), Cochrane Library (Wiley), and Scopus (Elsevier) databases. Databases were searched on March 18, 2021. For every publication that was taken into consideration, the data were charted independently by 2 reviewers, and in the event of a discrepancy, the principal investigator validated the choice of either extractor. Results were thematically described according to the 6 AQMH dimensions: acceptability, accessibility, appropriateness, effectiveness, efficiency, and safety. RESULTS: From 13,209 publications, 48 articles were included, largely from North American studies. Most studies measured treatment effectiveness (n=48, 100%) and acceptability (n=29, 60%) health quality dimensions. Over 80% (40/48) of studies investigated either a cognitive or exposure intervention for either posttraumatic stress disorder or a mood or anxiety disorder, generally indicating comparable results to in-person therapy. Safety (n=5, 10%) was measured in fewer studies, while treatment accessibility, appropriateness, and efficiency were not explicitly measured in any study, although these dimensions were mentioned as a future direction, hypothesis, or potential outcome. CONCLUSIONS: In relation to web-based therapist-delivered psychotherapies for those with a diagnosed mental illness, important aspects of health care quality (accessibility, appropriateness, efficiency, and safety) have received little scientific examination, underscoring a need to address these gaps. There are also significant issues related to the generalizability of this literature, including the underrepresentation of many geographic regions, cultures, populations, clinical contexts, and psychotherapy modalities. Qualitative research in underrepresented populations and settings may uncover important patient and contextual factors important for the future implementation of quality web-based psychotherapy.
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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 | 0.022 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".