Evaluation of User Satisfaction in a Supportive Text Message Program for Public Safety Personnel
Bibliographic record
Abstract
Introduction Public safety personnel (PSP) encounter traumatic events in their workplace, elevating the likelihood of mental health issues. Delivering efficient, evidence-backed interventions, like supportive SMS text messaging programs, can significantly enhance PSPs’ mental well-being, garnering high user satisfaction rates. Objectives This study evaluates users’ satisfaction, receptiveness, and perceptions of the supportive SMS text messaging intervention (Text4PTSI). Methods Participants enrolled in the Text4PTSI program and received one-way cognitive behavioural–based supportive text messages for six months. They participated in a web-based survey delivered through SMS text messages at enrollment, six weeks, three months, and six months after enrollment. The participants’ perceptions and receptiveness of the program were evaluated through a 5-point Likert scale. Data were represented as categorical variables, and overall satisfaction with the Text4PTSI program was assessed on a scale ranging from 0 to 100. Results Of the 131 Text4PTSI program subscribers, 81 participants responded to the survey, yielding 100 responses across the three follow-up time points. The average satisfaction score was 85.12 (SD 13.35). A significant portion of respondents, constituting 79%, agreed or strongly agreed that Text4PTSI helped them manage anxiety. Additionally, 72% reported relief from depressive symptoms, and 54% (54 out of 100 responses) felt less lonely. Moreover, the majority (84%) of participants expressed that Text4PTSI connected them to a support system, improving their mental well-being, felt more hopeful about managing concerns about their mental health or substance use (82%), and helped enhance their overall quality of life (77%). The data also revealed that most participants consistently read the supportive text messages (84 out of 100 responses, 84%), took time to contemplate each message (75 out of 100 responses, 75%), and revisited the messages more than once (76 out of 100 responses, 76%). Conclusions PSP participating in the 6-month Text4PTSI intervention expressed significant satisfaction and gratitude in the follow-up surveys. Their positive feedback indicates a promising path towards increased service utilization, potentially enhancing its effectiveness and impact on end users. Disclosure of Interest None Declared
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".