Video feedback: A novel application to enhance person‐centred dementia communication
Bibliographic record
Abstract
AIM: A discussion of the use of video feedback as an effective and feasible method to promote person-centred communication approaches within dementia care and long-term care. BACKGROUND: Effective strategies to integrate person-centred approaches into health care settings have attracted global attention and research in the past two decades. Video feedback has emerged as technique to enhance reflective learning and person-centred practice change in some care settings; however, it has not been tested in the context of person-centred dementia communication in long-term care. DESIGN: Discussion paper. DATA SOURCES: Articles dating from 1995 to 2018 retrieved via searches of the SCOPUS, CINAHL, MEDLINE and Cochrane Systematic Review databases. IMPLICATIONS FOR NURSING: Inclusion of video feedback in a person-centred dementia communication intervention for nurses and other health care providers may effectively fill a gap evident in the literature. This intervention can offer feedback of enhanced quality and enduring impact on behaviour change relative to traditional training. CONCLUSION: A critical review of empirical and theoretical evidence supports video feedback as a potential means to enhance person-centred communication within the context of dementia and long-term care. The promising benefits of video feedback present a novel research opportunity to pilot its use to enhance person-centred communication between nurses/health care providers and persons with dementia in long-term 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".