Advancing equitable access to digital mental health in the Asia-Pacific region in the context of the COVID-19 pandemic and beyond: A modified Delphi consensus study
Bibliographic record
Abstract
The COVID-19 pandemic had an unprecedented impact on global mental health and well-being, including across the Asia-Pacific. Efforts to mitigate virus spread led to far-reaching disruption in the delivery of health and social services. In response, there was a rapid shift to the use of digital mental health (DMH) approaches. Though these technologies helped to improve access to care for many, there was also substantial risk of access barriers leading to increased inequities in access to mental health care, particularly among at-risk and equity-deserving populations. The objective of this study was to conduct a needs assessment and identify priorities related to equitable DMH access among at-risk and equity-deserving populations in the Asia Pacific region during the first year of the COVID-19 pandemic. The study consisted of a modified Delphi consensus methodology including two rounds of online surveys and online consultations with stakeholders from across the region. Study participants included policy makers, clinicians and service providers, and people with lived experience of mental health conditions. Results demonstrate that vulnerabilities to negative mental health impacts and access barriers were compounded during the pandemic. Access barriers included a lack of linguistically and culturally appropriate DMH options, low mental health literacy and poor access to technological infrastructure and devices, low levels of awareness and trust of DMH options, and lack of policies and guidelines to support effective and equitable delivery of DMH. Recommendations to improve equitable access include ensuring that diverse people with lived experience are engaged in research, co-design and policy development, the development and implementation of evidence-based and equity-informed guidelines and frameworks, clear communication about DMH evidence and availability, and the integration of DMH into broader health systems. Study results can inform the development and implementation of equitable DMH as its use becomes more widespread across health systems.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".