‘Getting to Know Me’: the development and evaluation of a training programme for enhancing skills in the care of people with dementia in general hospital settings
Bibliographic record
Abstract
OBJECTIVE: The aims of the study were to report on the development and evaluation of a staff training intervention in dementia care designed for use in the general hospital setting: the 'Getting to Know Me' training programme. The study also aimed to undertake initial psychometric analysis on two new outcome scales designed to measure knowledge and confidence in dementia care. METHODS: The study comprised two phases. The first phase comprised the design of two questionnaires which are shared within this paper: Confidence in Dementia (CODE) Scale and Knowledge in Dementia (KIDE) Scale. In phase two, staff undertook the 'Getting to Know Me' training programme (n=71). The impact of the programme was evaluated using a pre-post design which explored: (1) changes in confidence in dementia; (2) changes in knowledge in dementia; and (3) changes in beliefs about challenging behaviour. RESULTS: The psychometric properties of the CODE and KIDE scales are reported. Statistically significant change was identified pre-post training on all outcome measures. Clinically meaningful change was demonstrated on the CODE scale. CONCLUSIONS: The 'Getting to Know Me' programme was well received and had a significant impact on staff knowledge and confidence. Our findings add to a growing evidence base which will be strengthened by further robust studies, the exploration of the impact of staff training on direct patient outcomes, and further identification of ways in which to transfer principles of care from specialist dementia environments into general hospital settings.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".