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Record W4253674038 · doi:10.24124/2016/bpgub1131

Staging relationships: using devised theatre to explore First Nations youths' experiences and perceptions of their relationships with healthcare providers

2016· dissertation· en· W4253674038 on OpenAlexaboutno aff
Julia Petrasek MacDonald

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careParticipatory action researchPerceptionCitizen journalismThe artsCommunity-based participatory researchPublic relationsPopulationNursingSociologyPsychologyMedicinePolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

In Canada, vast inequities exist between Aboriginal and non-Aboriginal youth, especially in northern, rural communities. Research has shown positive relationships with physicians greatly impacts on health, yet, to date, research on relationships between healthcare providers and Aboriginal peoples has not widely consulted or involved the younger population. The goal of this research was to explore relationships between Nisga'a First Nations youth (ages 19-25) and their healthcare providers by identifying cultural and social factors that encourage or hinder meaningful access to healthcare. Using a social determinants of health framework, this research employed community-based participatory approaches and decolonizing methodologies as well as arts-based methods (devised theatre). Four key themes emerged exemplifying two Nisga'a First Nations youths' experiences of relationships with healthcare providers. The most important finding was that Nisga'a youth participants identified "~relationships' as a determinant of their interactions with healthcare providers. Furthermore, using theatre proved to be a successful way to engage youth in research. --Leaf ii.

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)0.006
metaresearch head score (Gemma)0.005
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 score0.975
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0120.011
Scholarly communication0.0050.003
Open science0.0010.007
Research integrity0.0010.003
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 head0.142
GPT teacher head0.376
Teacher spread0.235 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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