Rehabilitation engagement is associated with lower level of care needs on discharge from postacute care in older adults with cognitive impairment.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: Older adults with cognitive impairment (CI) in postacute care (PAC) are at risk for an increased level of care (LOC) postdischarge. Rehabilitation engagement may impact the relationship between CI and increased LOC. RESEARCH METHOD/DESIGN: Ninety-two older veterans (> 50 years) were assessed by physical therapists or assistants with the Hopkins Rehabilitation Engagement Rating Scale (HRERS) while participating in Veterans Affairs PAC. Hierarchical logistic regression examined whether rehabilitation engagement predicted LOC while controlling for cognition as assessed with the Montreal Cognitive Assessment (MoCA). We then examined whether rehabilitation engagement moderated the effect of cognition on LOC. RESULTS: Hierarchical logistic regression modeling revealed that the HRERS total score predicted LOC after controlling for MoCA scores. The interaction between MoCA and HRERS total score was nonsignificant. Item-level HRERS analyses revealed a significant interaction for CI (MoCA score < 22) and active participation (HRERS Item 5). Examination of the interaction indicated that among low scorers on active participation, CI increased the odds of requiring higher LOC, while the main effect of CI on LOC was nonsignificant among those who scored high on active participation. CONCLUSION/IMPLICATIONS: Higher rehabilitation engagement reduced the risk of requiring higher LOC at PAC discharge after controlling for cognitive functioning. Additionally, active participation may buffer against adverse outcomes for older adults with CI. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".