A Meta-Analytic Investigation of the Association Between Working Alliance and Outcomes of Individual Career Counseling
Bibliographic record
Abstract
This article reports on the results of the first meta-analysis of the association between working alliance and outcomes of individual career counseling. This random-effects meta-analysis included 18 published and unpublished studies that produced a weighted mean effect size of r = .42. This effect size was heterogeneous across studies. Separate meta-analyses were conducted for several types of outcomes: Career outcomes, mental health outcomes, and client-perceived quality of the intervention. Average effect sizes for the association between working alliance and types of outcomes were .28, .18 and .62, respectively. Moderator analyses indicated that the overall mean effect size ( r =.42) varied in a large proportion as a function of the type of outcomes and the time of assessment of working alliance (first session, mid or at termination of the counseling service). Our results confirm that working alliance is associated to career counseling effectiveness and suggest that career counselors should emphasize on the working alliance during the career counseling process. In conclusion, this article provides suggestions for practice in individual career counseling and avenues of research on working alliance in this context.
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 | 0.039 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.039 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".