Home Care Services Use in Older Adults Living with Severe Mental Illness: Care Patterns Variations Before and After an Incident Dementia Diagnosis: Utilisation des services de soins à domicile chez les personnes âgées atteintes de troubles de santé mentale graves : Variation des modèles de soins avant et après un diagnostic de trouble neurocognitif
Bibliographic record
Abstract
OBJECTIVE: Older adults with severe mental illness (SMI) represent a complex population with various healthcare needs, even more so when they subsequently develop dementia. While home care (HC) services are advocated for both patients with SMI and dementia, little is known regarding real-life practices, especially for individuals having both conditions. Therefore, we aimed to describe healthcare use and transitions in older adults with SMI across HC user profiles, before and after an incident dementia diagnosis. METHOD: We used a retrospective cohort study drawn from Quebec health administrative data on individuals with SMI living in the community, aged 65 and older, and who received a first dementia diagnosis between 2013 and 2015. We described healthcare use 8 months prior and 2 years after the diagnosis, including hospital admissions, visits to the emergency department (ED), and long-term care (LTC) placement. RESULTS: A total of 3,713 individuals were included, 53% of whom were already receiving HC services before the diagnosis (Group 1), 28% received HC services only after the diagnosis (Group 2), and 19% did not receive any HC (Group 3). While Group 1 showed the highest overall healthcare use before the diagnosis, the most striking increase after the diagnosis was observed for Group 2, catching up with Group 1's levels for many indicators, and even surpassing them in some cases. HC was mainly introduced in the four months following the diagnosis in Group 2. Group 3, while showing the lowest healthcare use throughout the study period, had the second highest mortality rate after Group 1. Groups 2 and 3 were transferred to LTC and died at younger ages than Group 1, in average. CONCLUSIONS: This study highlights potential missed opportunities for intervention, such as an earlier HC introduction which could contribute to prevent an increase in hospitalizations and ED visits, or any HC in Group 3 to mitigate mortality risk and postpone LTC placement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".