Interventions to Support Mental Health among Those with Health Conditions That Present Risk for Severe Infection from Coronavirus Disease 2019 (COVID-19): A Scoping Review of English and Chinese-Language Literature
Bibliographic record
Abstract
This study aimed to address knowledge gaps related to the prevention and management of mental health responses among those with a condition that presents risk of severe COVID-19 infection. A scoping review that mapped English and Chinese-language studies (2019–2020) located in MEDLINE (Ovid), Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycInfo, Sociological Abstracts, Embase, China National Knowledge Infrastructure (CNKI), Wanfang Data, and Airiti Library was undertaken. Search terms related to COVID-19, mental health, and physical health were used and articles that included all three of these factors were extracted (n = 77). With the exception of one hospital-based pilot study, there were no intervention studies targeting mental health in those at risk of severe COVID-19 infection. Promising practices such as integrated care models that appropriately screen for mental health issues, address health determinants, and include use of digital resources were highlighted. Patient navigator programs, group online medical visits, peer support, and social prescribing may also support those with complex needs. Future policies need to address digital health access inequities and the implementation of multi-integrated health and social care. Furthermore, research is needed to comprehensively assess multi-integrated interventions that are resilient to public health crises.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".