Impact of a supportive text messaging program (Text4Support) for mitigating psychological problems in patients receiving formal mental health services: A randomized controlled trial
Bibliographic record
Abstract
Background: Text-based interventions are an innovative way to bridge gaps in mental health care. Programs like Text4Support provide accessible, cost-effective mental health support using daily messages based on cognitive behavioral principles, reaching a broad population. Objective: The study aims to evaluate the effectiveness of Text4Support in improving mental health outcomes compared to usual care in patients receiving formal mental health services. Methods: A randomized controlled trial was conducted on participants attending community mental health programs and those recently discharged from psychiatric inpatient and emergency care in Nova Scotia, Canada. Participants were assigned to either the Text4Support or control group. Mental health conditions were measured using validated scales. Patients in the Text4Support group received daily supportive text messages, whilst the control group received a single text message with a link to the Nova Scotia Health e-mental health resources. Results: Seven hundred eighty-one eligible patients were randomized, and 307 were included in the analysis. After adjusting for baseline scores, the third and ninth questions of the PHQ-9 showed a significant difference between the Text4Support group and the control group. When results were assessed based on self-reports from control group participants as to whether they accessed or did not access the NS Health e-mental health resource link, significant differences between the three groups were noted, with improvements for suicidal and/or self-harm ideation and disturbed sleep in the Text4Support group and deterioration observed in the two control groups. Conclusion: Text4Support addresses critical psychological symptoms like sleep disturbances and suicidal and/or self-harm ideation during vulnerable periods in patients' mental health journeys. Text4Support is a promising adjunct to conventional mental health care. Trial registration: ClinicalTrials.gov (NCT05411302). Registered on 6 June 2022.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".