Text4Support Mobile-Based Programming for Individuals Accessing Addictions and Mental Health Services—Retroactive Program Analysis at Baseline, 12 Weeks, and 6 Months
Bibliographic record
Abstract
Objective:Where traditional approaches fall short, widely accessible and accepted, yet under leveraged, digital technologies such as text messaging present novel opportunities to solve a range of health care solutions. The following provides a preliminary analysis of the Text4Support program, a text-messaging intervention using the principles of cognitive behavioral therapy, which seeks to support the health and well-being of individuals seeking support for addiction or mental health concerns. The goal of this study was to assess whether the Text4Support program improved the perceived overall mental well-being of participants. Methods:The evaluation analyzes survey responses of individuals who were enrolled in the Text4Support program beginning in July 2019, who had completed the 6-months program by May 2020. Participants were asked to provide responses to three surveys during their time in the program—at baseline, 12-weeks and 6-months, which included questions documenting demographic information, general satisfaction with the program, and a participants' level of “global distress” through use of the Clinical Outcomes Routine Evaluation System (CORE-10)—a validated brief 10-item assessment and outcome measurement tool used to assess conditions including anxiety, depression, physical problems, and risk to self. Results and Conclusions:This data set did not include a large enough sample of participants to reach statistical significance. Nevertheless, the study provides some preliminary analysis, and identifies opportunities for the future analysis and research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".