Upstream thinking and health promotion planning for older adults at risk of social isolation
Bibliographic record
Abstract
wilson d.m., harris a., hollis v. & mohankumar d. (2010) Upstream thinking and health promotion planning for older adults at risk of social isolation. International Journal of Older People Nursing6, 282–288 doi: 10.1111/j.1748‐3743.2010.00259.x Aims and objectives. To raise awareness of social isolation, and provide an approach to first conceptualise and then prevent social isolation among older community‐dwelling persons. Background. Older adults comprise a vulnerable population for social isolation and its associated health risks. Design. Literature review. Methods. Canada’s Population Health Promotion Model was chosen as a comprehensive tool to understand and prevent social isolation. Research studies were sought to identify key health determinants and evidence‐based options for preventing social isolation. Results. Around 1 out of 6 older persons are socially isolated and three health determinants are of prime importance: (i) income and social status; (ii) personal health practices and coping skills and (iii) social support networks. Evidence‐based interventions targeted to these health determinants are suggested. Conclusion. Nurses are a key group to advocate for actions needed to prevent social isolation. Implications for practice. Nurses can play a vital role in minimising social isolation through a variety of educational, prevention and political lobbying activities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".