The Application of e-Mental Health in Response to COVID-19: Scoping Review and Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic and its mitigation measures and impacts, such as shelter-in-place orders, social isolation, restrictions on freedoms, unemployment, financial insecurity, and disrupted routines, have led to declines in mental health worldwide and concomitant escalating demands for mental health services. Under the circumstances, electronic mental health (e-mental health) programs and services have rapidly become the "new normal." OBJECTIVE: The aim of this study was to assess key characteristics and evidence gaps in the e-mental health literature published in relation to the COVID-19 pandemic via a scoping review and bibliometric analysis. METHODS: We conducted a search of four academic databases (ie, MEDLINE, Embase, PsycInfo, and CINAHL) for documents published from December 31, 2019, to March 31, 2021, using keywords for e-mental health and COVID-19. Article information was extracted that was relevant to the review objective, including journal, type of article, keywords, focus, and corresponding author. Information was synthesized by coding these attributes and was then summarized through descriptive statistics and narrative techniques. Article influence was examined from Altmetric and CiteScore data, and a network analysis was conducted on article keywords. RESULTS: A total of 356 publications were included in the review. Articles on e-mental health quickly thrived early in the pandemic, with most articles being nonempirical, chiefly commentaries or opinions (n=225, 63.2%). Empirical publications emerged later and became more frequent as the pandemic progressed. The United States contributed the most articles (n=160, 44.9%), though a notable number came from middle-income countries (n=59, 16.6%). Articles were spread across 165 journals and had above-average influence (ie, almost half of the articles were in the top 25% of output scores by Altmetric, and the average CiteScore across articles was 4.22). The network analysis of author-supplied keywords identified key topic areas, including specific mental disorders, eHealth modalities, issues and challenges, and populations of interest. These were further explored via full-text analysis. Applications of e-mental health during the pandemic overcame, or were influenced by, system, service, technology, provider, and patient factors. CONCLUSIONS: COVID-19 has accelerated applications of e-mental health. Further research is needed to support the implementation of e-mental health across system and service infrastructures, alongside evidence of the relative effectiveness of e-mental health in comparison to traditional modes of care.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.055 |
| 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".