Bibliographic record
Abstract
There is a lack of health promotion for the elderly residing in long-term care facilities. It becomes imperative therefore for the nursing profession to readjust its thinking and practice to include health promotion strategies and interventions for institutionalized elderly in the long-term care sector. From using the empowerment theory in the discussion it is clear that nurses can help the elderly receive health promotion strategies and interventions to help the older person make informed choices about their lifestyle, health and treatment. The elderly population continues to grow and more than likely will spend some part of their life in a long-term care setting. Evidence from studies performed by Robertson (1991), Caserta (1995), Phillips (1994) and McBride (2000) suggests that health promotion efforts positively correlate with improved physical and psychological elements of health for the elderly. Nurses largely determine the kind of care that is given and thus can influence the quality of life that elderly client's experience. By advocating for health promotion interventions and strategies for their elderly clients, nurses are demonstrating to the community, their patients, and institution that nurses do have the power to make and influence change for the better. Nurses advocating for health promotion for their elderly patients not only empower choice, but also create an active and involved elderly population. Health promotion exists at all stages of life and does not cease when one is admitted to a long-term care facility (Hutchings, 1999; McBride, 2000).
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".