Needs, gaps and opportunities for standard and e-mental health care among at-risk populations in the Asia Pacific in the context of COVID-19: a rapid scoping review
Bibliographic record
Abstract
The COVID-19 pandemic is expected to have profound mental health impact, including in the Asia Pacific Economic Cooperation (APEC) region. Some populations might be at higher risk of experiencing negative mental health impacts and may encounter increased barriers to accessing mental health care. The pandemic and related restrictions have led to changes in care delivery, including a rapid shift to the use of e-mental health and digital technologies. It is therefore essential to consider needs and opportunities for equitable mental health care delivery to the most at-risk populations. This rapid scoping review: 1) identifies populations in the APEC region that are at higher risk of the negative mental health impacts of COVID-19, 2) identifies needs and gaps in access to standard and e-mental health care among these populations, and 3) explores the potential of e-mental health to address these needs.We conducted a rapid scoping review following the PRISMA Extension for Scoping Reviews (PRISMA-ScR). We searched Medline, Embase and PsychInfo databases and Google Scholar using a search strategy developed in consultation with a biomedical librarian. We included records related to mental health or psychosocial risk factors and COVID-19 among at-risk groups; that referred to one or more APEC member economies or had a global, thus generalizable, scope; English language papers, and papers with full text available.A total of 132 records published between December 2019 and August 2020 were included in the final analysis. Several priority at-risk populations, risk factors, challenges and recommendations for standard and e-mental health care were identified. Results demonstrate that e-mental health care can be a viable option for care delivery but that specific accessibility and acceptability considerations must be considered. Options for in-person, hybrid or "low-tech" care must also remain available.The COVID-19 pandemic has highlighted the urgent need for equitable standard and e-mental health care. It has also highlighted the persistent social and structural inequities that contribute to poor mental health. The APEC region is vast and diverse; findings from the region can guide policy and practice in the delivery of equitable mental health care in the region and beyond.
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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 | 0.042 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.029 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".