Older adult maintaining and improving health self-management through peer supported SMART goal setting
Bibliographic record
Abstract
Abstract Non-medical interventions to address risk factors (such as reducing smoking, increasing physical activity, and tackling limited social interaction) are needed to help tackle escalating social and financial health costs. Peer supported interventions have been used successfully to support persons’ health self-management; however, there is limited evidence for group interventions facilitated by older adults. A proof-of-concept study by the first author demonstrated the potential of older adults meeting in groups to each create and follow through with a single SMART goal for any area of health over one-month. This study extends SMART goal setting to enhancing health management over six months. Older adult participants from across Ontario will attend virtual SMART goal setting group sessions followed by six monthly support group meetings where they are free to choose any goal, whether a mitigation or a new behavior. Each month the facilitator will assist participants to continue, modify, or set a new goal. At the end participants will complete surveys about their satisfaction with the method, their results and their desire to continue with SMART goals. They will also be asked if they would like to facilitate new groups to continue the spread of peer-supported SMART goal groups. This study is designed to empower older adults to maintain or improve management of their physical, psychological, and/or social health. It will reveal the impact of an older adult created and guided group health intervention on feelings of self-efficacy and well-being.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".