Text4PTSI: A Promising Supportive Text Messaging Program to Mitigate Psychological Symptoms in Public Safety Personnel
Bibliographic record
Abstract
Background: Public safety personnel experience various mental health conditions due to their work’s complex and demanding nature. There are barriers to seeking support and treatment; hence, providing innovative and cost-effective interventions can help improve mental health symptoms in public safety personnel. Objective: The study aimed to evaluate the impact of Text4PTSI on depression, anxiety, trauma, and stress-related symptoms, and the resilience of public safety personnel after six months of providing supportive text message intervention. Methods: Public safety personnel subscribed to Text4PTSI and received daily supportive and psychoeducational SMS text messages for six months. Participants were invited to complete standardized self-rated web-based questionnaires to assess depression, anxiety, posttraumatic stress disorder (PTSD), and resilience symptoms measured on the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 scale (GAD-7), Posttraumatic Stress Disorder Checklist-Civilian Version (PCL-C), and the Brief Resilience Scale (BRS), respectively. The assessment of mental health conditions was conducted at baseline (enrolment) and six weeks, three months, and six months after enrollment. Results: One hundred and thirty-one subscribers participated in the Text4PTSI program, and eighteen completed both the baseline and any follow-up survey. A total of 31 participants completed the baseline survey and 107 total surveys were recorded at all follow-up time points. The baseline prevalence of psychological problems among public safety personnel were as follows: likely major depressive disorder (MDD) was 47.1%, likely generalized anxiety disorder (GAD) was 37.5%, low resilience was 22.2%, and likely PTSD was 13.3%. At six months post-intervention, the prevalence of likely MDD, likely GAD, and likely PTSD among respondents reduced; however, a statistically significant reduction was reported only for likely MDD (−35.3%, X2 (1) = 2.55, p = 0.03). There was no significant change in the prevalence of low resilience between baseline and post-intervention. There was a decrease in the mean scores on the PHQ-9, GAD-7, PCL-C, and the BRS from baseline to post-intervention by 25.8%, 24.7%, 9.5%, and 0.3%, respectively. However, the decrease was only statistically significant for the mean change in GAD-7 scores with a low effect size (t (15) = 2.73, p = 0.02). Conclusions: The results of this study suggest a significant reduction in the prevalence of likely MDD as well as the severity of anxiety symptoms from baseline to post-intervention for subscribers of the Text4PTSI program. Text4PTSI is a cost-effective, convenient, and easily scalable program that can augment other services for managing the mental health burdens of public safety personnel.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".