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Record W569527748 · doi:10.4324/9781315820194

Race, Culture and Psychotherapy

2014· book· en· W569527748 on OpenAlexaffabout
Roy Moodley, Stephen Palmer

Bibliographic record

Venuenot available
Typebook
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRace (biology)PsychotherapistPsychologySociologyGender studies

Abstract

fetched live from OpenAlex

What is multicultural psychotherapy? How do we integrate issues of gender, class and sexual orientation in multicultural psychotherapy? Race, Culture and Psychotherapy provides a thorough critical examination of contemporary multiculturalism and culturalism, including discussion of the full range of issues, debates and controversies that are emerging in the field of multicultural psychotherapy. Beginning with a general critique of race, culture and ethnicity, the book explores issues such as the notion of interiority and exteriority in psychotherapy, racism in the clinical room, race and countertransference conflicts, spirituality and traditional healing issues. Contributors from the United States, Britain and Canada draw on their professional experience to provide comprehensive and balanced coverage of the following subjects: critical perspectives in race and culture in psychotherapy governing race in the transference racism, ethnicity and countertransference intersecting gender, race, class and sexual orientation spirituality, cultural healing and psychotherapy future directions Race, Culture and Psychotherapy will be of interest not only to practicing psychotherapists, but also to students and researchers in the field of mental health and anyone interested in gaining a better understanding of psychotherapy in a multicultural society.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.015
GPT teacher head0.336
Teacher spread0.321 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations16
Published2014
Admission routes2
Has abstractyes

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