Patients’ Expectations and Experiences With a Mental Health–Focused Supportive Text Messaging Program: Mixed Methods Evaluation
Bibliographic record
Abstract
BACKGROUND: Web-based services are an economical and easily scalable means of support that uses existing technology. Text4Support is a supportive, complementary text messaging service that supports people with different mental health conditions after they are discharged from inpatient psychiatric care. OBJECTIVE: In this study, we aim to assess user satisfaction with the Text4Support service to gain a better understanding of subscribers' experiences. METHODS: This was a mixed methods study using secondary data from a pilot observational controlled trial. The trial included 181 patients discharged from acute psychiatric care and distributed into 4 randomized groups. Out of the 4 study groups in the initial study, 2 groups who received supportive text messages (89/181, 49.2% of patients), either alone or alongside a peer support worker, were included. Thematic and descriptive analyses were also performed. Differences in feedback based on sex at birth and primary diagnosis were determined using univariate analysis. The study was registered with ClinicalTrials.gov (trial registration number: NCT03404882). RESULTS: Out of 89 participants, 36 (40%) completed the follow-up survey. The principal findings were that Text4Support was well perceived with a high satisfaction rate either regarding the feedback of the messages or their perceived impact. Meanwhile, there was no statistically significant difference between satisfactory items based on the subscriber's sex at birth or primary diagnosis. The patients' initial expectations were either neutral or positive in relation to the expected nature or the impact of the text messages received on their mental well-being. In addition, the subscribers were satisfied with the frequency of the messages, which were received once daily for 6 consecutive months. The participants recommended more personalized messages or mutual interaction with health care personnel. CONCLUSIONS: Text4Support was generally well perceived by patients after hospital discharge, regardless of their sex at birth or mental health diagnosis. Further personalization and interactive platforms were recommended by participants that may need to be considered when designing similar future services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".