Predicting Outcomes of a Manualized Individual Career Counseling Intervention Over a One-Year Follow-Up From Trajectories of Change in Career Decision Difficulties
Bibliographic record
Abstract
This study tested whether trajectories of career decision difficulties identified in Milot-Lapointe and Le Corff (2023) predict outcomes of a manualized individual career counseling intervention 12 months after the intervention. Participants were 248 individuals who received an average of 7.79 sessions at a career counseling clinic and were reassessed 12 months after the intervention. Results showed that clients who experienced an optimal (Classes 1 and 2; 66% of clients) or a positive change but suboptimal (Class 3; 21% of clients) change during career counseling had negligible career decision difficulties 12 months after the intervention and were satisfied with their career decision, career situation and with counseling. Clients in Class 4, who did not experience any change during counseling (13% of clients), had significantly higher decision difficulties, were less satisfied with their career decision, career situation, counseling, and had lower life satisfaction at the 12-month follow-up compared to clients in the other classes. Results demonstrate the long-term utility of individual career counseling in producing, on average, sustainable positive outcomes for a large proportion of clients (87%). They also offer insights into the longitudinal consequences associated to variability in career counseling as clients who did not experience any change during counseling achieved poorer outcomes on the long run.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".