Psychological health and wellness and the impact of 6 weeks and 3 months supportive text messaging program (Wellness4MDs) among physicians and medical learners in Canada: a longitudinal study
Bibliographic record
Abstract
Background Physicians and medical learners face high rates of burnout, anxiety, and depression due to the demanding nature of their work. Many are reluctant to seek support because of stigma, time constraints, and limited access to care. Cognitive Behavioral Therapy (CBT)-based supportive SMS messaging offers a promising, scalable alternative. Objective This study evaluates the impact of Wellness4MDs, a CBT-based supportive messaging program, on the psychological health and well-being of physicians and medical learners in Canada. Methods Participants subscribed to the Wellness4MDs program and received daily supportive SMS messages for 3 months. Standardized self-rated web-based questionnaires assessing depression, anxiety, burnout symptoms were collected at baseline, 6 weeks, and 3 months using the PHQ-9, GAD-7, MBI, and WHO-5. Subscribers’ satisfaction was measured using an online, self-developed questionnaire adapted from tools previously employed to assess similar programs. Results A total of 806 subscribers participated, with 226 completing the baseline survey. 66 participants completed surveys at all follow-up points, and 53 completed both baseline and at least one follow-up survey. At the three-month follow-up, there were statistically significant reductions in mean scores for emotional exhaustion (EE) and anxiety symptoms (GAD-7), with reduction from baseline of 16.1% (t = 2.86, p = 0.01) and 15.5% (t = 2.05, p = 0.05) with effect sizes of 0.4 and 0.3 respectively, indicating moderate effects. These reductions remained statistically significant when missing data were imputed using the last observation carried forward (LOCF) method. However, no significant changes were observed on the PHQ-9 scale. The overall mean satisfaction score for the Wellness4MDs program was 7.98 (SD = 2.06). Most participants reported that the messages helped them cope with stress (72.7%), anxiety (70.5%), depression (51.1%), and loneliness (42.0%). Additionally, 71.6% felt more connected to a support system, and 78.4% reported improved overall mental well-being. Conclusion Wellness4MDs demonstrated effectiveness in reducing emotional exhaustion and anxiety symptoms. Its high user satisfaction, accessibility, and low-cost delivery model make it a promising complement to traditional mental health services for healthcare professionals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".