Physical disability and cognitive impairment among recipients of long-term care
Bibliographic record
Abstract
Background and objective: For practical policy purposes variables describing disability and impairment should be aggregated into broader factors. By using data from a Norwegian mandatory system the objective of this study was to analyse whether the number of factors describing the need for long-term care differs between recipients of home care and nursing home residents and according to the age or gender of long-term care recipients. The hierarchical order of the variables within each factor is determined to assess whether there are important informational gaps in the description of recipients. Methods: Data are from a mandatory system characterizing all recipients of public long term care in Norway. Two groups of public care recipients were included: elderly (67 years and older) individuals receiving home care services (N = 2,493) and patients in nursing homes (N = 1,218). Exploratory factor analysis (EFA), Confirmatory factor analysis (CFA) and item response analysis (IRT) were used to determine the number of factors and the hierarchical structures of the variables. Results: Two factors were sufficient to characterise need for both nursing home residents and home dwelling elderly. This result is not sensitive to stratification by age and gender. IRT analysis revealed large informational gaps suggesting that the used instrument fails to sufficiently capture important aspects of user needs. Conclusions: Factorization suggests that all elderly long term care users can be adequately described along two dimensions; on reflecting physical disability and one reflecting cognitive impairment. However, both the number of factors and the variable contained in each factor are likely to depend on the instrument used to characterise LTC users. Large informational gaps suggest a need to supplement the national information system used in Norway.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".