Spanish Adaptation of the Working Alliance Inventory (WAI). Psychometric properties of the patient and therapist forms (WAI-P and WAI-T)
Bibliographic record
Abstract
The working alliance is one of the most widely studied constructs in psychotherapy process research. The purpose of our study was to adapt the patient and therapist forms of the Working Alliance Inventory (WAI-P and WAI-T) into Spanish. Both measurement instruments were translated into Spanish through a systematic translation process. The psychometric properties of the instruments were evaluated in both a pilot study and a clinical study involving Spanish outpatients with depressive disorders and their therapists. In the clinical study, patients completed the Spanish-language Beck Depression Inventory (BDI) prior to initiating therapy and after the third and tenth psychotherapy sessions. High average scores were obtained with the Spanish-language WAI-P and WAI-T. A large number of individual items correlated satisfactorily with the overall score for the corresponding subscale. Both measures demonstrated excellent reliability (internal consistency) and convergent validity. There were some limitations in the discriminant validity of the measures vs. measures of empathy. Regarding predictive validity, the overall WAI-P and the Task subscale of the WAI-T separately explained a moderate percentage of the variance in patient change in the BDI after the tenth psychotherapy session. These results were satisfactory and consistent with those obtained in studies using the English-language WAI.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".