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Record W2803930197 · doi:10.18584/iipj.2018.9.2.2

The Sweat Lodge Ceremony: A Healing Intervention for Intergenerational Trauma and Substance Use

2018· article· en· W2803930197 on OpenAlexaffvenue
Teresa Naseba Marsh, David C. Marsh, Julie Ozawagosh, Frank Ozawagosh

Bibliographic record

VenueInternational Indigenous Policy Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsAssembly of First NationsNOSM University
Fundersnot available
KeywordsCeremonyIndigenousIntervention (counseling)Historical traumaMental healthSubstance usePsychologyMedicineCriminologyPsychotherapistPsychiatryHistoryEcologyArchaeology

Abstract

fetched live from OpenAlex

Many traditional healers and Elders agree that strengthening cultural identity, incorporating traditional healing practices, and encouraging community integration can enhance and improve mental health and reduce substance use disorders (SUD) in Indigenous populations. Despite the fact that traditional healing practices have always been valued by Indigenous Peoples, there is very little research on efficacy. Recent research by one of the authors in this group (T. Marsh) has shown that the blending of Indigenous traditional healing practices and a Western treatment model, Seeking Safety, resulted in a reduction in intergenerational trauma (IGT) symptoms and substance use disorders (SUD). This article focuses on the qualitative evidence concerning the impact of the traditional healing practices, specifically the sweat lodge ceremony. Participants reported an increase in spiritual and emotional well-being that they said was directly attributable to the ceremony. This study demonstrates that it would be beneficial to incorporate Indigenous traditional healing practices, including the sweat lodge ceremony, into Seeking Safety to enhance the health and well-being of Indigenous Peoples with IGT and SUD.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0160.000
Scholarly communication0.0010.001
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 head0.045
GPT teacher head0.381
Teacher spread0.336 · 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 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

Citations29
Published2018
Admission routes2
Has abstractyes

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