Helping staff to implement psychosocial interventions in care homes: augmenting existing practices and meeting needs for support
Bibliographic record
Abstract
OBJECTIVE: To contribute to an optimised training programme for care staff that supports the implementation of evidence-based psychosocial interventions in long-term care. METHODS: Qualitative study that involved focus group discussions with 119 care home staff within 16 care homes in the UK. Part of wider clinical trial aimed at developing and evaluating an effective and practical psychosocial intervention and implementation approach for people with dementia in long-term care. Inductive thematic analysis was used to identify themes and interpret the data. RESULTS: The findings highlighted that successful training and support interventions must acknowledge and respond to 'whole home' issues. Three overarching themes emerged as influential: the importance of contextual factors such as staff morale, interpersonal relationships within the home, and experience and perceived value of the proposed intervention. CONCLUSIONS: Priority must be given to obtain the commitment of all staff, management and relatives to the training programme and ensure that expectations regarding interaction with residents, participation in activities and the reduction of medication are shared across the care home.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".