Bibliographic record
Abstract
Hospital environments have been criticized as inadequate for meeting needs of patients with dementia. There is a need to explore innovative ways to involve frontline staff to make practical changes. Using videos to show compelling patient stories can be a powerful way for promoting frontline engagement in practice development. This poster reports the perspective of staff on using videos and reflexive groups to develop person-centred care in a medical unit. Methods consisted of video interviews with patients with dementia and 31 focus groups with a total of 50 staff, including nursing, physicians, and allied health. Five substantial themes emerged as important roles of the video reflexive groups in contributing to creating collective commitment and actions to improve dementia practice in the medical unit: (a) seeing through patients’ eyes, (b) seeing normal strange, (c) seeing inside and between, (d) seeing with others inspires actions, and (e) seeing team support builds a safe culture for learning. The findings suggest that videos reflexive groups can be an effective strategy for mobilizing positive change in acute hospital wards. In this study, staff participants described visual methods brought a fresh and practical approach to practice development in acute care.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".