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Record W2990046137

To Know Their Stories: Using Playbuilding to Develop a Training/Orientation Video on Person-Centered Care

2019· dissertation· en· W2990046137 on OpenAlexfundno aff
Kevin Hobbs

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2019
Typedissertation
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaBrock University
KeywordsOrientation (vector space)Training (meteorology)PsychologyMultimediaComputer scienceGeographyMathematics
DOInot available

Abstract

fetched live from OpenAlex

This study explores the experiences of health care staff and family members who provide support for people living with dementia and traumatic brain injury. Using a playbuilding methodology (Belliveau, 2006; Norris, 2009; Perry, Wessels & Wager, 2013) in which theatre performers devised short vignettes based on focus group interviews with health care providers, an educational video was produced. The video will be shown to the focus group interviewees in order to generate further conversation—knowledge co-creation—on the supportive and resistive practices in person-centred care (Leplege, Gzil, Cammelli, Lefeve, Pachoud & Ville, 2007; Kadri, Rapaport, Livingston, Cooper, Robertson & Higgs, 2018; Santana, Manalili, Jolley, Zelinsky, Quan & Lu, 2018), a philosophical approach that privileges the holistic needs of the individual rather than the bio-medical and administrative urgencies of the medical system. I outline the process of developing vignettes, videoing them and editing the video using a constructivist approach and an application of narrative and film theory. This work adds to the discussion of how the health care system may benefit from arts-based methods of knowledge construction.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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 designQualitative
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

Citations1
Published2019
Admission routes1
Has abstractyes

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