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Record W4412915500 · doi:10.55248/gengpi.6.0625.2420

Self-efficacy of Guidance Counselors in the use of eclectic interventions in counseling

2025· article· en· W4412915500 on OpenAlexfundno aff
Jorge Arellano

Bibliographic record

VenueInternational Journal of Research Publication and Reviews · 2025
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsnot available
FundersCanadian Nuclear Safety Commission
KeywordsPsychological interventionPsychologyPsychotherapistApplied psychologyClinical psychologyMedical educationMedicinePsychiatry

Abstract

fetched live from OpenAlex

This study explored the perspectives and challenges Registered Guidance Counselors (RGCs) faced with eclectic counseling.Among the CNSC RGCs, there was a wide range of knowledge, attitudes, and practical experiences, often shaped by each counselor's unique background and formal training.RGCs highlighted adaptability and individualized care as key, finding that no single therapeutic model adequately addressed the diversity of client issues.Over time, they shifted from relying on a single approach to integrating techniques tailored to individual needs, enhancing client empowerment and ensuring safety in urgent cases.They cited client engagement as essential to successful counseling and emphasized the counselor's role in maintaining high standards, ongoing education, and ethical conduct.On the other hand, through a review of 10 studies, key trends emerged: flexibility, e-counseling, youth-oriented therapy, integrative evaluation, and the impact of counselor attitudes on intervention choices.These trends underscored a growing shift toward flexible and client-centered eclectic approaches in counseling.Most RGCs felt confident in designing and implementing eclectic methods, with 80% expressing high self-efficacy in tailoring techniques to clients.This self-efficacy positioned RGCs well to address complex client needs in the dynamic mental health environment.

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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.281
GPT teacher head0.536
Teacher spread0.255 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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