User Satisfaction With a Daily Supportive Text Message Program (Text4PTSI) for Public Safety Personnel: Longitudinal Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Public safety personnel (PSP) are exposed to traumatic events due to their work environments, which increases the risk of mental health challenges. Providing effective and evidence-based interventions, such as SMS text messaging programs, can improve PSP's overall mental well-being with high user satisfaction rates. OBJECTIVE: This study aims to evaluate users' satisfaction, receptiveness, and perceptions of a cognitive behavioral therapy (CBT)-based supportive SMS text messaging intervention (Text4PTSI). METHODS: Participants self-subscribed to Text4PTSI and received unidirectional cognitive behavioral-based supportive text messages for 6 months. Participants completed a web-based survey delivered via SMS text message at enrollment, and 6 weeks, 3 months, and 6 months post enrollment. Respondents' perception and receptivity of the program were assessed using a questionnaire measured on a 5-point Likert scale. Data were collected as categorical variables, and overall satisfaction with the Text4PTSI program was measured on a scale from 0 to 100. RESULTS: There were 131 subscribers to the Text4PTSI program; however, only 81 subscribers responded to the survey, producing 100 survey responses across the 3 follow-up time points. The overall mean score of satisfaction was 85.12 (SD 13.35). More than half of the survey responses agreed or strongly agreed that Text4PTSI helped participants cope with anxiety (79/100 responses, 79%), depressive symptoms (72/100 responses, 72%), and loneliness (54/100 responses, 54%). Similarly, most of the survey responses agreed or strongly agreed that the Text4PTSI program made respondents feel connected to a support system, improved their overall mental well-being (84/100 responses, 84%), felt more hopeful about managing concerns about their mental health or substance use (82 out of responses, 82%), and helped enhance their overall quality of life (77/100 responses, 77%). The available survey responses suggest that the majority always read the supportive text messages (84/100 responses, 84%), took time to reflect on each message (75/100 responses, 75%), and returned to read the text messages more than once (76/100 responses, 76%). CONCLUSIONS: PSP who responded to the follow-up surveys reported high user satisfaction and appreciation for receiving the Text4PTSI intervention during the 6-month program. The reported satisfaction with the service provided could pave the way to ensuring a better uptake of the service with potential effectiveness to end users.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".