Implementation of the abilities‐focused approach to morning care of people with dementia by nursing staff
Bibliographic record
Abstract
Background. The efficacy of the abilities-focused approach to morning care has been demonstrated in two studies. However, the extent to which nurses are aware of and actually implement abilities-focused interventions in day-to-day practice is not known. Aim. The study aimed to determine the type and number of abilities-focused interventions delivered by nursing staff to residents with dementia during morning care. Methods. A one-group repeated measure design was used. Seventy-nine nursing staff attended an educational session to instruct them in the application of abilities-focused interventions. Data on the type and number of interventions used by nurses were obtained before, after and 3-months following attendance at the session. Data were collected through participants' self-report and observation. Results. Most nursing staff used abilities-focused interventions when providing morning care. Introduction to resident, orientation to resident and conversation with resident were three types of interventions most often applied over time. The number of interventions implemented increased after attendance at the education session and returned to baseline level at 3-month follow-up. Conclusions. Future research is recommended to examine the long-term effects of alternative designs of educational sessions.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".