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Record W4390679914 · doi:10.1521/jsyt.2023.42.3.58

Haudenosaunee Culture and Identity: Promoting Mental Health During the COVID-19 Pandemic

2023· article· en· W4390679914 on OpenAlexaffvenue
Rammiyaa Devanthan, Kahawani Doxtator, Dan Ashbourne, Jason Brown

Bibliographic record

VenueJournal of Systemic Therapies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsWestern University
Fundersnot available
KeywordsIndigenousThematic analysisMental healthPandemicCultural identityIdentity (music)Agency (philosophy)SociologyCoronavirus disease 2019 (COVID-19)Indigenous culturePsychologyQualitative researchMedicineSocial sciencePsychiatryNegotiation

Abstract

fetched live from OpenAlex

The COVID-19 pandemic had devastating effects on Indigenous communities worldwide. However, communities continued in various ways to preserve cultural connections and practice land and family-based activities central to health and well-being. This study was a collaborative effort between a university, a community agency, and a Haudenosaunee community to explore how culture and identity promoted mental health during the pandemic. A cultural program, Firekeepers, was delivered to promote and maintain connections and wellness. Semi-structured interviews with 19 community member participants focused on how knowing about your culture and identity helps you be mentally healthy. Data were analyzed using thematic analysis, and six themes were identified. Results add to the growing literature on the health restoring and affirming effects of culturally based belonging and participation in Indigenous cultural communities.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.985
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.006
Scholarly communication0.0030.001
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.357
Teacher spread0.327 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

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