A Non-Pharmacologic Approach to Manage Behaviours in Confused Medically Ill Older Adults in Acute Care
Bibliographic record
Abstract
BACKGROUND: Non-pharmacological interventions are recommended to manage challenging behaviours among cognitively impaired older adults, however few studies have enrolled patients in acute care. This study aimed to determine the feasibility of implementing non-pharmacological interventions to manage behaviours in hospitalized older adults. METHOD: A self-identity approach was used to identify potentially engaging activities for 13 older medically ill adults admitted to acute hospital; these activities were trialed for a two-week period. Data were collected on frequency of intervention administration and assistance required, as well as frequency of behaviours and neuroleptic use in the seven days prior to and following the trial of activities. RESULTS: Per participant, 5-11 interventions were prescribed. Most frequently interventions were tried two or more times (46%); 9% were not tried at all. Staff or family assistance was not required for 27% of activities. The mean number of documented behaviours across participants was 4.8 ± 2.3 in the pre-intervention period and 2.1 ± 1.9 in the post-intervention period. Overall the interventions were feasible and did not result in increasing neuroleptic use. CONCLUSION: Non-pharmacologic interventions may be feasible to implement in acute care. More research in this area is justified.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".