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Record W4408537283 · doi:10.1177/17579759251317517

Visual storytelling as democratizing knowledge: relational concepts of transdisciplinary health impact through film

2025· article· en· W4408537283 on OpenAlexafffundabout
Kate P. R. Dunn, Gary Hayes

Bibliographic record

VenueGlobal Health Promotion · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsRoyal Roads UniversityYork University
FundersCanadian Institutes of Health ResearchMitacs
KeywordsPraxisSociologyPublic relationsStorytellingParticipatory action researchContext (archaeology)TransdisciplinaritySocial mediaKnowledge translationKnowledge transferCitizen journalismEngineering ethicsKnowledge managementPolitical scienceNarrativeSocial scienceEngineering

Abstract

fetched live from OpenAlex

This paper examines the transdisciplinary collaboration between health practitioners, Indigenous community members, and doctoral researchers to democratize knowledge transfer enhancing social justice outcomes in the context of hepatitis C awareness with Indigenous communities in Alberta, Canada. Utilizing the impactful intersection between media and healthcare disciplines, two social science researchers built on each other's qualitative research projects using relational engagement and participatory action research to co-create a DocuStory film and accompanying impact campaign. Diverse expertise and varied life experiences contribute unique perspectives transferring insights informing knowledge translation. Communication scholars, media producers, and academics are exploring the social function of documentaries and how they can be used to generate change. This innovative collaboration draws on the strength and creativity of transdisciplinary relationships providing opportunity for social justice praxis at the intersections of culture, theory, health, and media. This successful approach is relevant for numerous health topics and inspires transdisciplinary collaboration and media innovation in health promotion.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.429
GPT teacher head0.712
Teacher spread0.282 · 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 designTheoretical or conceptual
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
Published2025
Admission routes3
Has abstractyes

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