Recommendations for staff education and training for older people with mental illness in long-term aged care
Bibliographic record
Abstract
BACKGROUND: This paper was written as a result of the International Psychogeriatric Association Task Force on Mental Health Services in Long-Term Care. The appraisal presented here aims to (1) identify the best available evidence that underpins best practice for geriatric mental health education and training of staff working in long-term care, and (2) summarize the appraisal of the literature to provide recommendations for practice. METHODS: An initial search of databases found 138 papers related to the search strategy. Selected papers were summarized and compared against set inclusion criteria. This resulted in 17 papers suitable for review. RESULTS: The majority of papers focused on behavior skills training. A number of key factors were identified that determine the success of geriatric mental health education and training and recommendations are outlined. CONCLUSIONS: Methodological weaknesses are common and highlight the need for further replication studies using strong research designs.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".