Risk Factors for Accidental Injuries WITHIN SENIOR CITIZENS' HOMES: Analysis of the Canadian Survey on Ageing and Independence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it