A Cross-Sectional Study Comparing Younger and Older Nursing Home Residents in Western Canada
Bibliographic record
Abstract
Abstract Specialized care for younger nursing home (NH) residents may be necessary to meet their unique health and quality of life needs; however, key attributes of younger NH residents are poorly understood and limit the development of effective, tailored interventions. This study described differences in clinical and nonclinical characteristics of younger vs. older nursing NH residents. In a retrospective cohort study, we used SPSS and analyzed comprehensive Resident Assessment Instrument – Minimum Data Set (RAI-MDS 2.0) data from NHs in Western Canada, for the period from January 2016 to December 2017. We included all assessments (full and abbreviated) performed quarterly. These findings indicated that younger (age 18-64) vs. older (age >=65) NH residents differed considerably: younger residents were predominately male, single, more obese, more depressed, had higher prevalence of depression, cerebral vascular accident, and hemi- or quadriplegia, and required more assistance in activities of daily living than older residents. The findings will contribute a better comprehension of the characteristics of the younger NH population and how they differ from other residents. The study provides useful information to policymakers, providers, and researchers to guide them in developing tailored policies, programs, and interventions. Also, findings may guide consumers as they plan for long-term care needs of loved ones. Finally, the findings provide a baseline estimate as researchers continue to track the growth of and changes in, the populations served in nursing homes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".