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Record W2172435839 · doi:10.1177/1049732315609569

Reframing Narratives of Aboriginal Health Inequity

2015· article· en· W2172435839 on OpenAlexaffabout
Andrew R. Hatala, Michel Desjardins, Amy Bombay

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsDalhousie UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsCognitive reframingHistorical traumaNarrativePsychological resilienceSociologyGlobeNarrative inquirySocial constructionismGender studiesPsychologySocial psychologySocial sciencePsychotherapist

Abstract

fetched live from OpenAlex

A large body of literature explores historical trauma or intergenerational trauma among Aboriginal communities around the globe. This literature connects contemporary forms of social suffering and health inequity to broader historical processes of colonization and the residential school systems in Canada. There are tendencies within this literature, however, to focus on individual pathology and victimization while minimizing notions of resilience or well-being. Through a social constructionist lens, this research examined how interpersonal responses to historical traumas can be intertwined with moments of and strategies for resilience. Detailed narrative interviews occurred with four Aboriginal Cree elders living in central Saskatchewan, Canada, who all experienced historical trauma to some extent. From this analysis, we argue that health research among Aboriginal populations must be sensitive to the complex individual and social realities that necessarily involve both processes of historical and contemporary traumas as well as resilience, strength, and well-being.

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)0.066
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0660.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0080.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.439
GPT teacher head0.653
Teacher spread0.213 · 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

Citations62
Published2015
Admission routes2
Has abstractyes

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