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Record W2725523663 · doi:10.1093/geroni/igx004.920

USING VIDEO REFLEXIVE GROUPS TO DEVELOP DEMENTIA PRACTICE

2017· article· en· W2725523663 on OpenAlexaff
Lillian Hung

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMultimedia Communication and Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReflexivityDementiaFocus groupPerspective (graphical)Unit (ring theory)NursingPsychologyHealth careMedicineMedical educationSociologyComputer science

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.165
GPT teacher head0.478
Teacher spread0.313 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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