Impact of a daily supportive text message program (text4hope) on the stress, anxiety, and depression levels of elderly (60 years and above) subscribers during the COVID-19 pandemic in Alberta.
Bibliographic record
Abstract
Introduction One of the biggest global crises in our generation is the COVID-19 pandemic. It has had a severe and far-reaching negative impact on our health systems, economies, and societies. Older adults were particularly at higher risk of severe illness, isolation from social distancing measures, and concerns about their health. Objectives The objective of this study is to evaluate the impact of the daily supportive text message program (Text4Hope) on the levels of stress, anxiety, and depression experienced by elderly subscribers during the COVID-19 pandemic in Alberta six weeks after enrollment. Methods An online survey link was used to gather demographic and clinical information on several self-report scales, such as the Perceived Stress Scale (PSS) ≥ 14 and Generalized Anxiety Disorder 7-item (GAD-7). Scale ≥ 10, and Patient Health Questionnaire-9 (PHQ-9) ≥ 10. Descriptive and inferential statistics were run using SPSS version 25. Results 172 subscribers out of 1136 completed baseline and six weeks using an online questionnaire, giving a response rate of 15.1%. There were significant reductions in mean scores on the PSS-10 and GAD-7 scales at six weeks compared to baseline (P>.05), but not on the PHQ-9 scale. There were also significant reductions in the prevalence of moderate or high stress (68.6% vs 60.5%, p=0.036) and likely GAD (14.9% vs 22.7%, p=0.029) from baseline to six weeks, with the highest reduction in stress (8.1%). A change (27.6% to 25.2%) in the prevalence of likely MDD from baseline to six weeks was insignificant. (P>.05) Conclusions This study’s findings show a decrease in the prevalence rates and the mean scores for stress and anxiety on standardized scales, indicating an improvement from baseline to six weeks. This outcome has potential implications for planning an intervention to meet the mental health needs of the elderly in similar situations like the pandemic 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".