Development, feasibility, and acceptability of a smartphone-based ecological momentary assessment of minority stress and suicidal ideation among sexual and gender minority youth
Bibliographic record
Abstract
OBJECTIVE: We sought to develop and assess the feasibility and acceptability of a smartphone-based ecological momentary assessment (EMA) study of minority stress and suicidal ideation intensity among sexual and gender minority youth (SGMY) in the US Southeast. METHODS: In Study 1, the EMA protocol was developed through an iterative process, incorporating qualitative input from focus groups and interviews with 16 parents of SGMY and 16 SGMY from the US Southeast as well as six clinicians and researchers. In Study 2, 50 SGMY aged 13-24 with past-year suicidal ideation and current depressive symptoms were recruited from the US Southeast. The study included a baseline assessment, 28 consecutive days of EMA surveys (3x per day), a weekly acceptability survey, and a post-study exit interview. RESULTS: In Study 1, qualitative feedback guided the selection, adaptation, and development of EMA measures and informed study features including the EMA schedule, reminder notifications, incentive structure, and the safety and risk monitoring protocol. In Study 2, the EMA protocol demonstrated feasibility through high compliance with the EMA survey (M = 80.21%, SD = 16.92%, Mdn = 83.93%, range = 38.10%-100.00%) with some variation over time and by participant age. Weekly feedback surveys indicated high acceptability, with participants reporting that the EMA surveys were easy to complete and private, understandable, minimally burdensome, and at least moderately engaging. Exit interviews revealed several themes, including facilitators of high engagement, barriers to engagement, intervention implications, and suggested improvements for future EMA studies. CONCLUSIONS: Smartphone-based EMA is a feasible and acceptable method for studying real-time experiences of minority stress and suicidal ideation intensity among SGMY at high risk. Incorporating community member feedback during EMA study development can help to ensure cultural responsiveness and enhance participant compliance. This paper provides practical guidance for researchers planning to conduct EMA suicide research with SGMY.
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 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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".