Bibliographic record
Abstract
BACKGROUND: Nutritional risk screening is typically done in clinical settings to identify individuals at risk of malnourishment. This article presents the first population-level assessment of nutritional risk based on a large national sample representative of Canadian householders aged 65 or older. DATA SOURCES AND METHODS: Data from the 2008/2009 Canadian Community Health Survey-Healthy Aging were used to estimate the prevalence of nutritional risk by selected characteristics. Factors associated with nutritional risk were examined with restricted and full logistic models. The distribution of responses on the SCREEN II-AB nutritional risk instrument is reported. RESULTS: Based on the results of the 2008/2009 survey, 34% of Canadians aged 65 or older were at nutritional risk. Women were more likely than men to be at risk. Among people with depression, 62% were at nutritional risk, compared with 33% of people without depression. Level of disability, poor oral health, and medication use were associated with nutritional risk, as were living alone, low social support, infrequent social participation, and not driving on a regular basis. Lower income and education were also associated with nutritional risk. INTERPRETATION: Nutritional risk is common among seniors living in private households in Canada. The characteristics of people most likely to be at nutritional risk provide evidence for targeted screening and assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".