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Record W4388526165 · doi:10.1080/10640266.2023.2271201

Ayahuasca ceremony leaders’ perspectives on special considerations for eating disorders

2023· article· en· W4388526165 on OpenAlexaff
Meris Williams, Annie Miller, Adèle Lafrance

Bibliographic record

VenueEating Disorders · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsLaurentian University
Fundersnot available
KeywordsAyahuascaCeremonyIndigenousPsychologyMental healthEating disordersIntervention (counseling)MedicinePsychotherapistSociologyPsychiatryAnthropologyHistoryArchaeology

Abstract

fetched live from OpenAlex

Eating disorders (EDs) are difficult conditions to resolve, necessitating novel treatments. Ayahuasca, a psychedelic plant medicine originating in Indigenous Amazonian communities, is being investigated. Aspects of ceremonial ayahuasca use (purging, dietary restrictions) appear similar to ED behaviors, raising questions about ayahuasca's suitability as an intervention for individuals with EDs. This study explored the perspectives of ayahuasca ceremony leaders on these and other considerations for ceremonial ayahuasca drinking among individuals with EDs. A qualitative content analysis of interviews was undertaken with 15 ayahuasca ceremony leaders, the majority of whom were from the West/Global North. Screening for EDs, purging and dietary restrictions, potential risks and dangers, and complementarity with conventional ED treatment emerged as categories. The findings offer ideas, including careful screening and extra support, to promote safe and beneficial ceremony experiences for ceremony participants with EDs. More research is needed to clarify the impacts of ceremony-related purging and preparatory diets. To evolve conventional models of treatment, the ED field could consider Indigenous approaches to mental health whereby ayahuasca ceremony leaders and ED researchers and clinicians collaborate in a decolonizing, bidirectional bridging process between Western and Indigenous paradigms of healing.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.064
GPT teacher head0.359
Teacher spread0.295 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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