Risk Factors for Accidental Injuries WITHIN SENIOR CITIZENS' HOMES: Analysis of the Canadian Survey on Ageing and Independence
Bibliographic record
Abstract
Using data from the Survey on Ageing and Independence (SAI), risk factors for unintentional injuries occurring within the homes of individuals older than 65 are identified. For the SAI, conducted by Statistics Canada in 1991, data were collected on a representative sample of approximately 20,000 individuals between ages 45 and 102. For each household contacted, one individual older than 45 was interviewed via the telephone. For the present analysis, only individuals older than 65 (n = 10,059) were used. Approximately 5% of senior citizens experienced an injury that limited their activity for at least 1 day. Using logistic regression, the following risk factors for injury were identified: education, alcohol consumption, smoking, rest and sleep patterns, support, and interactions between age and gender, activity limitations and age, and home maintenance and gender. The present findings are important to the body of research concerning injuries among older adults. The results expand current univariate analysis of data identifying risk factors for injuries within the literature and provide comprehensive information pertaining to risk factors for accidental injuries at the multivariate level. Identification of risk factors provides health care professionals, particularly front line nurses, with insight into factors that, if modified, have the potential to decrease accidental injuries and improve or maintain quality of life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".