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Record W3110541136 · doi:10.18584/iipj.2020.11.4.8623

The Impact of Training Indigenous Facilitators for a Two-Eyed Seeing Research Treatment Intervention for Intergenerational Trauma and Addiction

2020· article· en· W3110541136 on OpenAlexaffvenue
Teresa Naseba Marsh, David C. Marsh, Lisa M. Najavits

Bibliographic record

VenueInternational Indigenous Policy Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsNOSM University
Fundersnot available
KeywordsIndigenousIntervention (counseling)Psychological interventionAddictionPsychologyHistorical traumaAnxietyMedicinePsychiatryNursing

Abstract

fetched live from OpenAlex

Intergenerational trauma in Indigenous Peoples was not the result of a targeted event, but rather political and governmental policies inflicted upon entire generations. The resultant effects of these traumas and multiple losses include addiction, depression, anxiety, violence, self-destructive behaviors, and suicide, to name but a few. Traditional healers, Elders, and Indigenous facilitators agree that the reclamation of traditional healing practices combined with conventional interventions could be effective in addressing intergenerational trauma and substance use disorders. Recent research has shown that the blending of Indigenous traditional healing practices and the Western treatment model Seeking Safety resulted in a reduction of intergenerational trauma (IGT) symptoms and substance use disorders (SUD). This article focuses on the Indigenous facilitators who were recruited and trained to conduct the sharing circles as part of the research effort. We describe the six-day training, which focused on the implementation of the Indigenous Healing and Seeking Safety model, as well as the impact the training had on the facilitators. Through the viewpoints and voices of the facilitators, we explore the growth and changes the training brought about for them, as well as their perception of how their changes impacted their clients.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.000
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 head0.120
GPT teacher head0.480
Teacher spread0.360 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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