From the Toronto Rehabilitation Institute (Drs
Bibliographic record
Abstract
Objective: The objective of this study was to identify the cognitive symptoms that HCPs find difficult to manage in dementia patients, and the strategies that they report using when pa-tients exhibit these symptoms. Subjects and Methods: One hundred thirty-three HCPs (ie, nurses, therapists, dieticians, social workers) in 7 GRUs in Ontario, Canada, completed a ques-tionnaire focused on the frequency of cognitive symptoms that persons with dementia experi-enced after hip fracture surgery and on the strategies HCPs used to manage these symptoms. Results: The data collected indicate that HCPs perceived patients ’ memory impairment, lack of insight, and lack of ability to carry out purposeful movement to be the main symptoms that in-terfere with their ability to rehabilitate patients. Fifty percent of nursing staff and 100 % of allied HCPs listed strategies they used when patients exhibited these cognitive symptoms. Strategies staff used when patients displayed cognitive symptoms included providing visual and verbal re-minders, adjusting the environment and routines, and offering consistent routines and supervi-sion. Conclusions: The findings from this study indicate that HCPs on GRUs caring for patients with cognitive impairment who have had a hip fracture frequently encounter cognitive symp-toms that hinder their care delivery. Rehabilitation staff require knowledge about how to assess
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.323 | 0.060 |
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".