Nursing Home Social Services Directors Caring for Residents With Serious Mental Illness
Bibliographic record
Abstract
Abstract Psychosocial care for residents with serious mental illness (SMI) requires understanding of co-morbidities and careful attention to needs, rights, and preferences. Analyses of social services directors (SSDs) responses (n=924) to the National Nursing Home Social Service Director Survey considered perceived roles and competence to provide care stratified by the percentage of NH residents with SMI. Depression screenings and biopsychosocial assessments were common roles regardless of the percentage of residents with SMI. About one-quarter lacked confidence to train colleagues in recognizing distinctions between depression, delirium and depression (23.4% unable) or to develop care plans for residents with SMI (26% unable). A bachelor’s degree (OR=0.64, 95% CI:0.43, 0.97) or less (OR= 0.47, 95% CI:0.25, 0.89) was associated with less perceived competence in care planning compared to those with a master’s degree. SSDs reported less involvement in referrals or interventions for resident aggression in homes with a high proportion of residents with SMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".