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Counseling Arab and Chaldean American Families

2007· article· en· W142411091 on OpenAlexaff
Julie Hakim‐Larson, Ray Kamoo, Sylvia C. Nassar‐McMillan, John H. Porcerelli

Bibliographic record

VenueJournal of Mental Health Counseling · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAcculturationEthnic groupPsychological interventionMental healthCultural diversityPopulationDiversity (politics)PsychologyMedicinePsychiatrySociologyAnthropology

Abstract

fetched live from OpenAlex

The last century has seen an increase in the population of Americans of Arab and Chaldean descent. In recent decades, clinicians have articulated the goal of enhancing their knowledge of cultural diversity for the purpose of improving their appreciation for diversity and the quality of their mental health interventions with diverse populations. However, there is currently little systematic empirical research regarding the counseling of Arab and Chaldean Americans, although awareness of the need for such research among mental health professionals has started to emerge. The purpose of this paper is to provide an integrative review of the values and socio-cultural forces that are relevant to the counseling of this population in North America, and to provide some culturally sensitive recommendations for working with American families of Arab and Chaldean ethnicity. In particular, we propose that effective interventions with clients of Arab and Chaldean ethnic backgrounds will need to be informed by an understanding of the everyday sociopolitical contextual background of target clients and the impact of values and acculturation processes on the family network.

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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
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)0.0000.000

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 head0.029
GPT teacher head0.406
Teacher spread0.377 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations39
Published2007
Admission routes1
Has abstractyes

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