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Record W7115596499 · doi:10.82396/cjcd.v1i1.2963

Effective Career Counselling: Relationship Between Work Personality, Learning Style and Client Intervention Preferences

2021· article· en· W7115596499 on OpenAlexaff

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsMemorial University of NewfoundlandCommunity Sector Council Newfoundland and Labrador
Fundersnot available
KeywordsPreferenceIntervention (counseling)Style (visual arts)PersonalityCareer counselingWork (physics)Process (computing)

Abstract

fetched live from OpenAlex

Determining client preference regarding the structure and approach to the career counselling intervention could be expected to benefit the counselling relationship, especially when working with clients who are resistant to typical intervention approaches. A process is proposed that seeks to provide offenders with a choice between two approaches to group career counselling, one that is action-oriented and a second that is grounded in self-reflection. This study focused on the development of an assessment tool that included work personality and learning style in the process of determining the individual’s preference for career counselling. This instrument, the Career Counselling Preferences Questionnaire (CCPQ), along with Holland’s Self-Directed Search (SDS-E) and Kolb’s Learning Style Inventory (LSI) was administered to 60 inmates, parolees, and probationers to investigate these inter-correlations and to determine the validity of the CCPQ in assessing preferences for counselling structure. Four Holland types, Artistic, Investigative, Social and Enterprising, were found to be positively correlated with a “thinking” approach to career intervention. The Social type was found to be additionally correlated with a “doing” approach. The Realistic Holland type, accounting for the largest portion of the sample, was found to be not significantly correlated with either approach, as was the Conventional type.

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)0.002
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 score0.020
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.263
Teacher spread0.233 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

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