Factor Structure of the Short Version of the Working Alliance Inventory and Its Longitudinal Measurement Invariance Across Individual Career Counseling Sessions
Bibliographic record
Abstract
The aim of the present study was 2-fold: (a) to examine the factor structure of the short version of the Working Alliance Inventory (WAI-S) in clients who were engaged in individual career counseling sessions and (b) to investigate whether the factor structure of the WAI-S is invariant across the first and the third career counseling sessions. A total of 283 clients seeking individual career counseling completed the WAI-S at the end of the first session (T1). Of the 283 clients, 217 also completed the WAI-S at the end of the third session (T2). Confirmatory factor analyses were performed to assess the fit of one-factor, two-factor, three-factor, and bilevel hierarchical models. The results showed that the three-factor and the bilevel hierarchical models had the best fit to the data at both T1 and T2. The factor structure of the WAI-S was invariant across the first and the third career counseling sessions. Results suggest that researchers and clinicians can use the WAI-S knowing that it adequately measures Bordin’s theoretical model of working alliance in the specific context of individual career counseling.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".