Cognition, Neurocognitive disorder, Psychotropic Group of Medication, and Fall among Canadians Aged 65+ Years
Bibliographic record
Abstract
Despite the high incidence of fall among the elderly in residential care, this problem has been minimally explored and reported. This study examined the association between (cognitive performance, dementia, four groups of psychotropic medication-hypnotics, antidepressants, antianxiety medication, and antipsychotics) and fall among Canadian elderly in residential care. A multi–variate logistic regression was used for the analysis. The Social Cognitive Theory was used to interpret the findings of an analysis on 180,231 Canadian residents from 2018–2019. Cognitive performance at intact level (OR=1.114, p<0.001), cognitive performance at moderate level (OR=1.192, p<0.001), dementia diagnosis (OR=1.075, p<0.001), antipsychotics (OR=2.571, p<0.001), antidepressants (OR =1.486, p<0.001) and antianxiety prescription (OR=3.284, p<0.001) increased the odds ratio of fall. However, cognitive performance at severe level (OR=0.898, p<0.001), no dementia diagnosis (OR=0.001, p<0.001) and hypnotics prescription (OR=0.389, p<0.001) decreased the odds ratio of fall. Findings indicate that cognitive performance at the intact and moderate levels, antianxiety, antipsychotic and antidepressant medications and dementia were strong predictors of fall among the elderly in residential care in Canada. A limitation of the study was that the dataset used captured data from 7 out of 13 Canadian provinces and territories therefore, limiting external validity. The potential positive social change impact of this study is that it would guide caregivers, enhance fall prevention practices and decrease fall occurrence in this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".