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Record W4396868966 · doi:10.5430/jct.v13n2p113

Predictability of Extrinsic and Intrinsic Factors on Counseling Competence of School Counselors: A Cross-sectional Study in Vietnam

2024· article· en· W4396868966 on OpenAlexvenueno aff
Vũ Thu Trang, Tran Thanh, Nguyen Thi Mai Lan, Vu Dung

Bibliographic record

VenueJournal of Curriculum and Teaching · 2024
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyPredictabilityCompetence (human resources)PsychologyMedical educationDevelopmental psychologyClinical psychologyMedicineSocial psychologyPhysics

Abstract

fetched live from OpenAlex

This study aims to explore the impact of extrinsic and intrinsic factors on the counseling competence of Vietnamese school counselors. A cross-sectional study was conducted on 226 teachers in two Vietnamese provinces. All school counselors have attended school counseling training courses with an average experience of 11.3 years. Linear regression analysis was used to determine the impact of extrinsic and intrinsic factors on counseling competence. The results show that: (i) When considering separately the impact’s possibility of each factor, the model has confirmed the possibility of a positive impact of both factors; (ii) however, when considering the model combining two groups of factors, the role of variables in the extrinsic factors no longer exists, while the role of variables in the intrinsic factors is still available. This study has confirmed the role of intrinsic factors related to the personal counseling competence of school counselors. This finding allows us to propose a solution to improve and develop counseling competence for school counselors focusing on identified intrinsic factors and promoting policies to maintain the positive impact of extrinsic factors in Vietnamese education or countries with similar educational backgrounds.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.360
Teacher spread0.329 · 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 designObservational
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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