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Record W4403500047 · doi:10.26522/ssj.v18i3.4667

Storywork to Decolonize Mental Health: Recentering Indigenous Histories in Canada, Kenya and Australia

2024· article· en· W4403500047 on OpenAlexafffundvenueabout
Lorena Jonard, S. Hegarty, Mohamed Ibrahim

Bibliographic record

VenueStudies in Social Justice · 2024
Typearticle
Languageen
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsUniversity of British ColumbiaYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousMental healthDecolonizationPolitical scienceGender studiesSociologyPsychologyPsychiatryLaw

Abstract

fetched live from OpenAlex

Colonization has had extremely negative impacts on the mental health and wellness of Indigenous peoples throughout the world. In this paper we take up colonial processes as they relate to Indigenous lives and mental health in three contexts: Canada, Kenya and Australia. This work engages storytelling and the method of storywork (Archibald et al., 2019) as a way to preserve and pass on history and as a way of resisting colonial oppression. This work is grounded in an intersectional approach to social justice and decolonization (Crenshaw, 1990; Hankivsky & Cormier, 2011), and supported sharing, knowledge co-creation and joint thematic narrative analysis of Indigenous experiences of mental health and justice systems across the three contexts. Our writing team represents a collaborative process between Indigenous and non-Indigenous authors where the members of the team most impacted by colonization use stories to reflect on the impact of colonization and its specific ties to psychiatric, justice and child welfare systems. This paper is presented in three main parts beginning with “Emile’s Story,” followed by “Remembering ‘Is That You Ruthie?’” and concluding with “Navigating Kenya’s Colonial Legacy.” This work engages a process of decolonization by challenging these destructive colonial narratives through storytelling. This paper will both document and demonstrate the importance of creating space for different forms of knowledge creation within academia.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.103
GPT teacher head0.451
Teacher spread0.348 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2024
Admission routes4
Has abstractyes

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