Older adults’ reasons for applying to a nursing home – a document analysis
Bibliographic record
Abstract
BACKGROUND: Ageing in place is the social norm in Sweden, yet older adults apply for a nursing home on a daily basis which suggests that ageing in place needs further study as it is not suitable for everyone. Aim: to study descriptions of older adults' reasons for applying to a nursing home in documents of granted nursing home decisions. MATERIALS AND METHODS: One hundred and sixty decisions were analyzed through document analysis with a deductive content analysis using the Canadian Model of Occupational Performance-Engagement (CMOP-E) as a framework. RESULTS: Reasons for applying were represented in the three factors of the CMOP-E. In personal factors, reason for applying was e.g. connected to severe anxiety. In environmental factors, family culture had an influence in the application. In occupational factors, the ability to perform self-care and mobility greatly affected decisions to apply to nursing homes. CONCLUSION: Descriptions of the older adults' activities in daily life were limited. If OTs were further involved in nursing home applications, adults ageing in place could be better supported and a move to a nursing home may be prevented. Significance: this study contributes to the understanding of why older adults chose to apply to a nursing home.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".