A Text Message Intervention for Adolescents With Depression and Their Parents or Caregivers to Overcome Cognitive Barriers to Mental Health Treatment Initiation: Focus Groups and Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Many adolescents with depression do not pursue mental health treatment following a health care provider referral. We developed a theory-based automated SMS text message intervention (Text to Connect [T2C]) that attempts to reduce cognitive barriers to the initiation of mental health care. OBJECTIVE: In this two-phase study, we seek to first understand the potential of T2C and then test its engagement, usability, and potential efficacy among adolescents with depression and their parents or caregivers. METHODS: In phase 1, we conducted focus groups with adolescents with depression (n=9) and their parents or caregivers (n=9) separately, and transcripts were examined to determine themes. In phase 2, we conducted an open trial of T2C comprising adolescents with depression referred to mental health care (n=43) and their parents or caregivers (n=28). We assessed usability by examining program engagement, usability ratings, and qualitative feedback at the 4-week follow-up. We also assessed potential effectiveness by examining changes in perceived barriers to treatment and mental health care initiation from baseline to 4 weeks. RESULTS: In phase 1, we found that the themes supported the T2C approach. In phase 2, we observed high engagement with daily negative affect check-ins, high usability ratings, and decreased self-reported barriers to mental health treatment over time among adolescents. Overall, 52% (22/42) of the adolescents who completed follow-up reported that they had attended an appointment with a mental health care specialist. Of the 20 adolescents who had not attended a mental health care appointment, 5% (1/20) reported that it was scheduled for a future date, 10% (2/20) reported that the primary care site did not have the ability to help them schedule a mental health care appointment, and 15% (3/20) reported that they were no longer interested in receiving mental health care. CONCLUSIONS: The findings from this study suggest that T2C is acceptable to adolescents with depression and most parents or caregivers; it is used at high rates; and it may be helpful to reduce cognitive barriers to mental health care initiation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".