From the Toronto Rehabilitation Institute
Bibliographic record
Abstract
Objective: Although evidence suggests that patients with cognitive impairment can benefit from rehabilitation, healthcare professionals (HCPs) on geriatric rehabilitation units (GRUs) of-ten find that providing care to these patients following a hip fracture can be challenging. The objective of this study was to identify the behavioral symptoms that HCPs find difficult to man-age in patients with dementia who have had a hip fracture and the strategies that they report using when patients exhibit these symptoms. Subjects and Methods: One hundred thirty-three HCPs responsible for providing direct rehabilitation care in 7 GRUs in Ontario, Canada, completed a questionnaire. The questionnaire collected data on the frequency of behavioral symptoms that persons with dementia experienced after hip fracture surgery and on the strate-gies HCPs used to manage these symptoms. Results: The data collected indicate that HCPs perceived patients ’ anxiety, agitation, and irritability to be the main behavioral symptoms that interfere with their ability to deliver rehabilitation care. HCPs perceived that patients ’ behav-iors occurred frequently enough to influence rehabilitation care, however, only 51 % of nursing staff listed strategies they used when patients exhibited behavioral symptoms, whereas as many as 96 % of allied HCPs listed strategies. When clients had symptoms, staff used assessment and
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.542 | 0.192 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".