Mental health Apps to address inequitable access to care in specific regions of the global North and South: A scoping review
Bibliographic record
Abstract
Introduction: There is a recent growth in the development of mental health applications (MHAPPs) to reduce stigma, improve knowledge and facilitate access to care especially in the area of common mood disorders. Yet, it remains unclear whether such interventions can address the access to care gap equitably in the global North and South. Such understanding could provide insights for mental health innovations during the COVID-19 pandemic as well. Methods: Using Arksey and O’Malley’s methodical framework, a scoping review was conducted on academic and grey literature published during 2015 and 2019. The countries of India and China were selected as exemplar for the global South and Canada and US for the global North. The reviewed literature was synthesized through thematic analysis and employed the social determinants of health lens. Results: 20 articles were selected for full-text review. The results reveal that MHAPPs for depression and anxiety are efficacious in improving symptoms across the examined regions. Outcome scores (Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, flourishing scale, social interaction anxiety scale) improved in 13 studies. Yet, public awareness in the global North and logistical barriers (mental health stigma/discrimination, financial and social challenges, usability of apps, and cultural barriers to self-care) in the global South inhibit uptake. Conclusion: Awareness of MHAPPs and logistical barriers must be addressed to make MHAPPs more accessible. Policy makers should be cautious in implementing MHAPPs in disadvantaged communities given several challenges. A broader policy level emphasis is needed to address the logistical capabilities and cultural sensitivity of MHAPPs. The findings are also discussed in relation to the digital innovations for mental health in the pandemic. Given the focus of the presented review on specific regions, the transferability of findings warrant caution.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".