Digital health interventions for suicide prevention among LGBTQ: A narrative review
Bibliographic record
Abstract
Background: Suicidal thoughts and behaviors (STBs) are prevalent within the Lesbian, Gay, Bisexual, Transgender, and Queers (LGBTQ) community, often exacerbated by challenges in accessing care and the perceived stigma and discrimination tied to disclosing one's identity. Digital health interventions that offer psychosocial self-help present a promising platform to reach individuals at risk of STBs, especially those who may not engage with conventional health services. This review aimed to assess the role of digital-based intervention in reducing STBs among LGBTQ individuals. Methods: We conducted a systematic literature search from three databases, PsycINFO, PubMed, and CINHAL, from 1st Jan 1990 to 31st December 2023. The review encompassed studies investigating the feasibility, acceptability, and impact of digital interventions on STBs, employing randomized control trials (RCTs), pseudo-RCTs, observational pre-posttest designs, and qualitative studies. Potential bias was evaluated using the McGill Mixed Methods Appraisal Tool (MMAT). Results: Five non-overlapping studies were included, reporting data from 777 participants. The studies featured diverse types of digital interventions, including videos, online writing, and mobile applications. The studies included three RCTs, and two qualitative studies. Across most of these studies, notable enhancements or reductions in the proportion of participants reporting STBs were observed post-intervention, alongside improvements in help-seeking intentions. The findings underscored that the applications used in the studies were engaging, acceptable, and deemed feasible in effectively addressing suicide prevention among the LGBTQ community. Conclusion: Overall, digital interventions were found to be feasible and acceptable in suicide prevention among LGBTQ communities, demonstrating preliminary efficacy in increasing help-seeking behavior when experiencing suicidal thoughts and in reducing STBs. Therefore, advocating for widespread promotion and dissemination of digital health interventions is crucial, particularly in low- and middle-income countries (LMICs) with limited access to health services and heightened barriers to obtaining such services. Further research using fully powered RCT is imperative to assess the efficacy of these interventions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".