Profile of Residents with Mental Disorders in Canadian Long-Term Care Facilities: A Cross-Sectional Study
Bibliographic record
Abstract
<strong>Brief Summary:</strong> Residents in long-term care facilities in Canada with mental and cognitive disorders have complex care needs. To meet these needs an integrated model of care is recommended. <strong>Context:</strong> The high prevalence of mental disorders in residents of long-term care (LTC) facilities raises serious concerns for facility operators and staff. These residents have multiple vulnerabilities that facility staff should have the necessary knowledge and skills to properly meet their needs. <strong>Objectives:</strong> To describe the profile of residents with mental disorders (MD) and Alzheimer’s Disease and Related Dementias (ADRD) in Canadian long-term care (LTC) facilities. <strong>Findings:</strong> Seventy-six percent of residents had MD (40%) and ADRD (36%). These residents compared to those without such disorders were more likely to be cognitively impaired, manifest aggressive behavior, receive psychotropic drugs, and physically restrained, and less likely to be socially engaged. <strong>Strengths and Limitations:</strong> The large representative sample was a key strength. The findings add to the knowledge about the profile of LTC residents. The cross-sectional design of the study limits the findings to the population studied. <strong>Implications:</strong> Residents with MD and ADRD compared to those without such disorders are highly vulnerable because of their double burden of mental and physical comorbidities. Their profile may be of interest to LTC facility operators, clinicians, and policy makers about their complex care needs. Our findings raise awareness of the need for trained LTC facility staff for knowledge and skills in psychogeriatric conditions to assess, plan, and implement appropriate interventions for these residents. Coordinated and integrated models of care with access to psychogeriatric specialists such as psychiatrists or advanced practice nurses will also be of benefit to them.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".