A Meta‐Analysis of the Effectiveness of Individual Career Counseling on Career and Mental Health Outcomes
Bibliographic record
Abstract
ABSTRACT This article reports on the results of the first meta‐analysis on the effectiveness of individual career counseling. This random‐effects meta‐analysis included 35 independent samples that produced a weighted mean effect size of g = 0.82 for career outcomes and g = 0.68 for mental health outcomes. These effect sizes were heterogeneous across samples. Categorical meta‐regressions indicated that five intervention components significantly predicted career counseling effects on career or mental health outcomes. These five components are psychoeducation concerning the decision process, cognitive restructuring, written exercises (occupational analyses), individualized feedback on career choice, and attention to decreasing potential obstacles. Our results suggest that individual career counseling can be a valuable mental health intervention when clients’ mental health difficulties are intertwined with career concerns. They also highlight the importance that individual career counseling incorporates the five critical intervention components identified in this study to foster positive career or mental health outcomes.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.045 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".