Trajectories of Change in Career Decision Difficulties During a Manualized Individual Career Counseling Intervention: The Influence of Counselor Adherence, Working Alliance and Client Personality Traits
Bibliographic record
Abstract
This study aimed to identify trajectories of change in client career decision difficulties during a manualized career counseling intervention and examine the role of counselor adherence, working alliance, and personality traits in predicting these trajectories. Participants were 257 individuals who received an average of 7.79 career counseling sessions at a university career services center. Using growth mixture modeling, four class-trajectories were identified. Clients in class 1 had a moderate level of decision difficulties at the beginning of counseling while clients in classes 2, 3 and 4 had moderate-salient initial levels of difficulties. Clients in classes 1 and 2 experienced a very large reduction of their decision difficulties during counseling and left the process with negligible levels of difficulties. Clients in class 3 saw a large reduction of their decision difficulties during counseling and left the process with moderate levels of difficulties. Clients in class 4 did not experience change and left the process with moderate-salient levels of difficulties. Counselor adherence to the intervention manual significantly contributed to discriminate between clients from class 4 and clients from classes 1, 2 and 3. Client level of neuroticism significantly contributed to distinguish clients belonging to class 4 from clients belonging to class 1.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".