Cross-sectional survey evaluating Text4Mood: mobile health program to reduce psychological treatment gap in mental healthcare in Alberta through daily supportive text messages
Bibliographic record
Abstract
BACKGROUND: To complement the oversubscribed counselling services in Alberta, the Text4Mood program which delivers daily supportive text messages to subscribers was launched on the 18th of January, 2016. This report presents an evaluation of self-reports of the impact of the program on the mental wellbeing of subscribers. METHODS: An online link to a survey questionnaire was created by an expert group and delivered via text messages to mobile phones of all 4111 active subscribers of the Text4Mood program as of April 11, 2016. RESULTS: Overall, 894 subscribers answered the survey (overall response rate 21.7 %). The response rate for individual questions varied and is reported alongside the results. Most respondents were female (83 %, n = 668), Caucasian (83 %, n = 679), and diagnosed with a psychiatric disorder (38 %, n = 307), including Depression (25.4 %, n = 227) and Anxiety (20 %, n = 177). Overall, 52 % (n = 461) signed up for Text4Mood to help elevate their mood and 24.5 % (n = 219) signed up to help them worry less. Most respondents felt the text messages made them more hopeful about managing issues in their lives (81.7 %, n = 588), feel in charge of managing depression and anxiety (76.7 %, n = 552), and feel connected to a support system (75.2 %, n = 542). The majority of respondents felt Text4Mood improved their overall mental well-being (83.1 %, n = 598). CONCLUSION: Supportive text messages are a feasible and acceptable way of delivering adjunctive psychological interventions to the general public with mental health problems. Given that text messages are affordable, readily available, and can be delivered to thousands of people simultaneously, they present an opportunity to help close the psychological treatment gap for mental health patients in Alberta and elsewhere.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".