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Record W2337133491 · doi:10.1177/1049732315609572

Creating an Ethnodrama to Catalyze Dialogue in Home-Based Dementia Care

2015· article· en· W2337133491 on OpenAlexafffund
Mark Speechley, Ryan DeForge, Catherine Ward‐Griffin, Nicole M. Marlatt, Iris Gutmanis

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSt Joseph's Health CareWestern University
FundersCanadian Institutes of Health Research
KeywordsDementiaPsychologyNursingGerontologyMedicineSociologyDisease

Abstract

fetched live from OpenAlex

This article describes the development of a theater script derived from a critical ethnographic study that followed people living with dementia--and their family and professional caregivers--over an 18-month period. Analysis of the ethnographic data yielded four themes that characterized home-based dementia care relationships: managing care resources, making care decisions, evaluating care practices, and reifying care norms. The research team expanded to include a colleague with playwright experience, who used these themes to write a script. A theater director was included to cast and direct the play, and finally, a videography company filmed the actors on a realistic set. To contribute to the qualitative health research and the research-based theater knowledge translation literatures, this article describes and explains the creative decisions taken as part of our effort to disseminate research focused on home-based dementia care in a way that catalyzes and fosters critical (actionable) dialogue.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0170.035
Scholarly communication0.0120.011
Open science0.0030.017
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.948
GPT teacher head0.824
Teacher spread0.124 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2015
Admission routes2
Has abstractyes

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