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Record W4408881139 · doi:10.55016/ojs/jet.v57i2.80573

Practicum Supervisors' Role in the Development of Counselling Psychology Graduate Students' Cultural and Social Justice Responsiveness

2024· article· en· W4408881139 on OpenAlexaff
Birdie J Bezanson, Veronica C. Shim, Anusha Kassan, Charis Falardeau

Bibliographic record

VenueJournal of educational thought. · 2024
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsUniversity of British ColumbiaAcadia University
Fundersnot available
KeywordsPracticumPsychologySocial justiceGraduate studentsPedagogyCultural psychologyCounseling psychologySocial psychologyApplied psychologyMedical educationSociologyCriminologyMedicine

Abstract

fetched live from OpenAlex

Abstract: The importance of cultural and social justice responsiveness (CSJR) in counselling psychology training is no longer disputed. Training models that have been put forth are mainly conceptual and less is known about how to support students to translate theory to practice. To understand this process more clearly, this study elicited the experiences of 16 field supervisors who were supporting the development of CSJR in counselling psychology practicum students. The Enhanced Critical Incident Technique (ECIT) was employed to guide and analyze in-depth, open-ended qualitative interviews with clinical supervisors. To guide this study, three research questions were designed: 1) What supervision experiences are helpful to students’ development of CSJR? 2) What supervision experiences are unhelpful to students’ development of CSJR? 3) What supervision experiences would be desirable in students’ development of CSJR? These results are discussed in relation to current literature on CSJR, and implications and future directions are proposed.

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)0.011
metaresearch head score (Gemma)0.027
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 score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
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 head0.113
GPT teacher head0.471
Teacher spread0.358 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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