Entering Canadian long-term care with obesity: Exploring initial assessment data
Bibliographic record
Abstract
The Canadian population is aging, and rates of obesity are also on the rise. These demographic changes have implications for the long-term care (LTC) system in Canada that need to be better understood, yet little is known about the population with obesity in Canadian LTC. In this thesis, an exploratory analysis of residents newly entering LTC between 2010 and 2020 is provided. Cross-tabulations and chi-squared statistical testing (p≤0.001) were employed to analyze retroactive, consecutive cross-sectional initial assessment data (N=350,348) from the Canadian Institute for Health Information’s (CIHI) Community Care Reporting System (CCRS) to explore the levels of obesity, demographic characteristics (age, sex, primary language, rural or urban previous residency), rates of health conditions, and independence and assistance levels of activities of daily living (ADLs). Over the full study period, the rate of obesity for the population entering LTC was 19%, and 7% entered with at least class II obesity (≥35 kg/m2). Rates of obesity and BMI obesity categories tended to increase incrementally over the course of the study period. Those entering LTC with obesity were more likely to be younger, female, English/French speaking, and arriving from rural areas. Individuals with obesity had lower rates of dementia and higher levels of independence when performing ADLs. They also exhibited higher rates of diabetes and a greater need for two+ person assistance for ADLs. This thesis begins to fill in the gap in our understanding of the population with obesity in LTC, providing a broad picture of the heterogeneous nature of the population, including important differences in health and ADL profiles across the three obesity BMI categories (i.e., classes I-III).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".