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Record W2171253778

From Ancestral Knowledge to Clinical Practice: The Case of <em>Agonias</em> and Portuguese Clinicians in America

2006· article· en· W2171253778 on OpenAlexaff
Susan James, Geoffrey S. Navara, Jonathon Lomotey Wilfrid, Juanne N. Clarke

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2006
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPortugueseMeaning (existential)PsychologyMental healthImmigrationAnxietyDepression (economics)PsychotherapistPsychiatryClinical psychologyHistory
DOInot available

Abstract

fetched live from OpenAlex

Cultures have varying notions about symptom expression and the treatment of mental health issues. Consequently, clients and psychotherapists may or may not share a similar worldview. In the psychotherapy literature there has been increased attention to these complex processes. This survey descriptive study aims to understand how therapists working with culturally diverse clients incorporate sensitivity to cultural differences. Fifteen culturally sensitive mental health care providers working with the Portuguese immigrant community were interviewed about their practices. Specifically, we investigated their understanding of the symptoms, causes and cures for agonias, a culture specific phenomenon. It was found that even though the providers are all Portuguese themselves, the meaning that they ascribed to agonias (anxiety and/or depression) was very different than the meaning ascribed to agonias by community members. The community member’s meanings ranged from indigestion to being on the brink of death. A cluster analysis revealed that clinicians who stated that agonias is anxiety, conducted cognitive behavioral therapy or psychopharmacology, and those that stated agonias had a depressive component tended to use family therapy or psychoanalysis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.339
Teacher spread0.306 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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