A Student-Led Community Outreach Telehealth Program for COVID Education and Health Promotion (COACH)
Bibliographic record
Abstract
Abstract While public health measures of quarantining, socially isolating, and physical distancing are important to minimize the spread of coronavirus (COVID-19), these actions may also compromise the ability to manage one’s own health, thereby increasing the risk of adverse health events. The purpose of this study was to evaluate a student-delivered Community Outreach teleheAlth program for Covid education and Health promotion (COACH) to community-living adults (age ≥65 years). We hypothesized that COACH would improve health promoting behaviour as measured by the Health Directed Behaviour subscale of the Health Education Impact Questionnaire. We also anticipated COACH would improve secondary outcomes of perceived stress, depressive and anxiety (Depression, Anxiety, and Stress Scale-21), social support (Medical Outcomes Study Social Support Survey), health-related quality of life (Short Form-36), and health promotion self-efficacy (Self-Rated Abilities for Health Practices Scale). In this single-group pre-post study, we recruited 75 community-living adults with access to telephone/video-conferencing technology to participate in six 30-45 minute sessions with trained medical students over a two-month period. The mean age of participants was 72.4 years (58.7% female), with 80% reporting two or more chronic conditions. No participants were diagnosed with COVID-19 during participation. Paired sample t-tests showed significant improvement in health directed behaviour (p < .001, d = 0.45) and self-efficacy (p <.001, d = 0.44), but significant decrease in mental health-related quality of life (p < .001, d = -1.69). Overall, COACH may help improve health directed behaviour and health promotion self-efficacy, despite decreases in mental health possibly associated with COVID-19 restrictions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".