The opportunity for e-mental health to overcome stigma and discrimination
Bibliographic record
Abstract
Introduction Many with mental illness do not seek treatment, often due to stigma; be it public, self, or institutional type. To improve outcomes, stigma needs addressing. Objectives Understand the opportunity for e-mental health to help overcome stigma and, to provide an expert opinion to foster its adoption. Methods We conducted literature searches using the terms ((mental health) AND ((stigma) OR (discrimination))) AND (((((digital tools) OR (digital services)) OR (healthcare apps)) OR (digital solutions)) OR (digital technology)), limited to 2007 – 2023, identifying 223 citations, 9 of which were relevant for this evaluation, including 4 systematic reviews ( Table 1 ). Results Literature reports suggest that e-mental health may be useful for addressing stigma and reducing the treatment gap. While it was not consistently as good as face-to-face services, e-mental health tools were frequently shown to be effective in reducing stigma, improving mental health literacy, and increasing help-seeking behaviors. Tools included web-based breathing, meditation, and CBT; suicide prevention apps; and online videos and games. Experts from a 2022 global Think Tank session convened by eMHIC, opined and emphasised that embracing e-mental health must not leave people behind nor reinforce inequality and that structural barriers must first be acknowledged and overcome. Creating a shared understanding of the challenge and of terminology is essential, as is codesigning any solution together with people with lived experience. Table 1. Systematic literature reviews Study Interventions Findings SLR + meta-analysis, 9 studies, n=1916 (Goh et al. Int J Ment Health Nu 2021;30:1040–1056) - Web-based program - MIDonline - AboutFace - BluePages - MoodGYM - MHFA eLearning - Beyond Silence Online vs offline: similarly effective for reducing public stigma SLR, healthcare setting (Pospos,et al. Acad Psychiatry 2018;42:109–120) - Breath2Relax - Headspace - Meditation Audios - MoodGYM - Stress Gym - Virtual Hope Box - Stay Alive Identified tools provide a starting point to mitigate burnout, depression, and suicidality SLR, 13 interventions for stigma (Johnson, et al. Indian J Psychol Med 2021;44:332–340) - Web-based, psychoeducation interventions - Online games - Mobile app Most interventions increased help-seeking SLR + meta-analysis, 9 RCTs, n=1832 (Rodriguez-Rivas, et al. JMIR Serious Games. 2022; 10:e35099) - Video games - Virtual reality - Videoconferencing and online chat Interventions had a consistent effect on reducing public stigma Conclusions Published data suggest that e-mental health is promising to reduce stigma and discrimination, with the potential to foster help-seeking and treatment engagement. Adoption requires attention to derailers and must foster inclusivity. There is an imperative to adopt e-mental health, especially evidence-based solutions. Disclosure of Interest K. Subramaniam Employee of: Employee of Viatris, A. Greenshaw: None Declared, A. Thapliyal: None Declared
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".