Effective Career Counselling: Relationship Between Work Personality, Learning Style and Client Intervention Preferences
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".