Subscribers’ Perspectives and Satisfaction with the MoreGoodDays Supportive Text Messaging Program and the Impact of the Program on Self-Rated Clinical Measures
Bibliographic record
Abstract
Background: Young adults (18 to 30 years of age) are confronted with numerous challenges, such as academic stressors and peer pressure. The MoreGoodDays program was co-designed with young adults to alleviate psychological issues, improve their mental well-being and provide support for young adults in Alberta during the COVID-19 pandemic and beyond. Objective: The current study aimed to explore subscribers’ perspectives and satisfaction with the MoreGoodDays supportive text messaging program and the impact of the program on self-rated clinical measures. Methods: Subscribers of the MoreGoodDays program were invited via a link delivered in a text message to complete online evaluation surveys at six weeks, three months and six months. Program perception and satisfaction questions were adapted from those used to evaluate related programs. Anxiety, depression and PTSD symptoms were respectively assessed using the Generalized Anxiety Disorder-7 scale, the Patient Health Questionnaire-9 scale and the PTSD Civilian Checklist 5, and resilience levels were assessed using the Brief Resilience Scale (BRS). Data were analyzed with SPSS version 26 for Windows utilizing descriptive and inferential statistics. Results: There was a total of 168 respondents across the three follow-up time points (six weeks, three months and six months). The overall mean satisfaction with the MoreGoodDays program was 8.74 (SD = 1.4). A total of 116 (69.1%) respondents agreed or strongly agreed that MoreGoodDays messages helped them cope with stress, and 118 (70.3) agreed the messages helped them cope with loneliness. Similarly, 130 (77.3%) respondents agreed that MoreGoodDays messages made them feel connected to a support system, and 135 (80.4) indicated the program helped to improve their overall mental well-being. In relation to clinical outcomes, the ANOVA test showed no significant differences in mean scores for the PHQ-9, GAD-7 and PCL-C scales and the BRS from baseline to the three follow-up time points. In addition, there was no statistically significant difference in the prevalence of likely GAD, likely MDD, likely PTSD and low resilience at baseline and at six weeks. Conclusions: Notwithstanding the lack of statistically significant clinical improvement in subscribers of the MoreGoodDays program, the high program satisfaction suggests that subscribers accepted the technology-based intervention co-created with young adults, and this offers a vital tool to complement existing programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".