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Record W3008225020 · doi:10.1111/ijn.12820

Video feedback: A novel application to enhance person‐centred dementia communication

2020· article· en· W3008225020 on OpenAlexaff
Deanne J O’Rourke, Michelle Lobchuk, Genevieve Thompson, Christina Lengyel

Bibliographic record

VenueInternational Journal of Nursing Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDementiaPsychologyComputer sciencePhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.421
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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