Does the Person-Centered Care Model Support the Needs of Long-Term Care Residents With Serious Mental Illness and Intellectual and Developmental Disabilities?
Bibliographic record
Abstract
Person-centered care approaches continue to evolve in long-term care (LTC). At the same time, these settings have faced increased challenges due to a more diverse and complex population, including persons with intellectual and developmental disabilities (IDD) and serious mental illness (SMI). This study examined the mental, social, and physical wellbeing of residents with different diagnoses, within a person-centered care model. It was hypothesized that individual wellbeing would be comparable among all residents, regardless of primary diagnosis. The study cohort was drawn from all admissions to long-term care facilities in the USA from 2011 to 2013. Data are based on admission, 3 and 6 month follow-up Minimum Data Set (MDS) 3.0 assessments. The groups examined included: schizophrenia, other psychotic disorders, IDD, dementia, and all others (i.e., none of the above diagnoses). The wellbeing outcomes were depression (mental), pain (physical), and behaviors (social). All residents experienced improvements in pain and depression, though the group without the examined diagnoses experienced the greatest gains. Behaviors were most prevalent among those with psychotic disorders; though marked improvements were noted over time. Improvement also was noted among persons with dementia. Behavior worsened over time for the three other groups. In particular, those with IDD experienced the highest level of worsening at 3-month follow-up, and continued to worsen. The results suggest person-centered care in US nursing homes provides the necessary foundation to promote mental and physical wellbeing in persons with complex needs, but less so for social wellbeing.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".