The effectiveness of CBT-based daily supportive text messages in improving female mental health during COVID-19 pandemic: results from the Text4Hope program
Bibliographic record
Abstract
Introduction The COVID-19 pandemic has significantly exacerbated gender disparities in mental health, particularly impacting women. To address this, Alberta, Canada, launched Text4Hope, a Cognitive Behaviour Therapy-based text messaging intervention, to provide support and resources for psychological challenges during the pandemic. This study aimed to assess the effectiveness of Text4Hope in reducing stress, anxiety, depression, sleeping disturbances, and suicidal ideation among female subscribers during the COVID-19 pandemic. Methods The study employed both an uncontrolled longitudinal design and a controlled cohort design. The uncontrolled longitudinal study analyzed a one-year dataset ( n = 9,545) of clinical outcomes, comparing mean differences in mental health symptoms from baseline to 6 weeks after subscription. The controlled cohort design compared two groups, with ( n = 1,763) and without ( n = 567) intervention exposure during the same period. Data were collected through self-administered online surveys completed at baseline and six weeks after subscription. Sociodemographic information and validated scales (e.g., 10-item Perceived Stress Scale (PSS-10), Generalized Anxiety Disorder (GAD-7), and Patient Health Questionnaire (PHQ-9)) were used to assess mental health outcomes. Results The results from the longitudinal study indicated a significant reduction in anxiety prevalence and anxiety symptoms, with a 19.63% decrease in GAD-7 mean score and a 32.02% decrease in likely anxiety from baseline to six weeks. Depressive symptoms and perceived stress also showed a significant reduction ( p < 0.001), albeit to a lesser extent. In the controlled cohort study, the intervention group had significantly ( p < 0.001) lower PHQ-9 [19.5 (SD 7.05)], GAD-7 [7.5 (SD 5.27)], and CMH [35.53 (SD 18.45)] scores. Additionally, the study found substantial differences ( p < 0.001) in suicidal ideation (26.1 vs. 15.7) between groups but no significant differences in sleep disruption. Discussion These findings suggest that Text4Hope could be an effective intervention for reducing stress, depression, suicidal ideation, and particularly anxiety symptoms among women during public emergencies. The study provides valuable insights into the potential benefits of text messaging interventions in supporting mental health during crisis situations.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".