Relationship between restraint use, engagement in social activity, and decline in cognitive status among residents newly admitted to long‐term care facilities
Bibliographic record
Abstract
AIM: Declining cognitive function can negatively affect residents' quality of life (QOL) in long-term care facilities (LTCFs). The present study examined the role of physical restraint use, use of antipsychotic medications, and engagement in social activities to affect change in cognitive status and drive cognitive decline among residents newly admitted to a LTCF. METHODS: Secondary data analysis used interRAI Minimum Data Set 2.0 data gathered at admission and first follow-up assessment (n = 111,052). The interRAI Minimum Data Set 2.0 collects comprehensive information as part of regular clinical care, and is mandated for all LTCF in Ontario, Canada. Bivariate and logistic regression analyses investigated the roles of physical restraint use, antipsychotic medication use and social engagement affecting cognition, and were stratified based on the presence/absence of diagnosis of dementia. RESULTS: At follow up, 16.1% of residents (n = 16 414) showed decline in cognition. Residents with one or more physical restraints (chair, trunk and limb) were at increased risk for cognitive decline evidenced among residents with and without a diagnosis of dementia. Antipsychotic medication use did not emerge as a strong predictor of cognitive decline. Social engagement was protective against cognitive decline, and more pronounced for residents without a diagnosis of dementia. CONCLUSION: Physical restraint use should be avoided, or used as a last resort. LTCFs should prioritize resident engagement in social activities in either formal activities or ad hoc, as soon as possible on entry to the LTCFs. Prioritizing social networks and greater participation in activities might decrease the risk for cognitive decline, thereby improving or maintaining resident quality of life. Geriatr Gerontol Int 2017; 17: 246-255.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".