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Record W2750160631 · doi:10.1177/0011000017719458

Make It Personal: A Qualitative Investigation of White Counselors’ Multicultural Awareness Development

2017· article· en· W2750160631 on OpenAlexaff
Shawna Atkins, Marilyn Fitzpatrick, Gauthamie Poolokasingham, Mariane Lebeau, Lisa B. Spanierman

Bibliographic record

VenueThe Counseling Psychologist · 2017
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyEmpathyTransformative learningCourseworkMulticultural educationCultural competenceMulticulturalismGrounded theoryPersonal developmentQualitative researchPedagogySocial psychologySociologyPsychotherapist

Abstract

fetched live from OpenAlex

In this qualitative research study, we explored the multicultural awareness development of 12 multiculturally adept non-Latino White counselors. Using a grounded theory approach, we found that early personal experience with diversity was the most important contributing factor in developing understanding and empathy for oppression among White counselors. This factor appeared to lay the foundation for an ongoing personal initiative to develop multicultural awareness. Subsequently, counselors tried to maximize what they could learn from their culturally diverse clients, work environments, coursework, supervision, and mentoring opportunities. Their personal initiative also inspired them to persevere despite the difficult emotions and conflict inherent in this developmental process. Results suggest the need to incorporate personally transformative experiences in counselor training and to prepare counselors for the emotional challenges of multicultural awareness development.

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)0.013
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.009
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.160
GPT teacher head0.442
Teacher spread0.282 · 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 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

Citations31
Published2017
Admission routes1
Has abstractyes

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