Measuring Changes in Counselor Self-Efficacy: Further Validation and Implications for Training and Supervision
Bibliographic record
Abstract
This study examines the changes in counselor self-efficacy beliefs during training. For this purpose, the Counseling Self-Estimate Inventory (COSE), based on Bandura's self-efficacy theory (1986 Bandura , A. ( 1986 ). Social foundations of thought and action . Englewood Cliffs , NJ : Prentice-Hall . [Google Scholar]), was employed (Larson et al.,1992 Larson , L. M. , Suzuki , L. A. , Gillespie , K. N. , Potenza , M. T. , Bechtel , M. A. , & Toulouse , A. L. ( 1992 ). Development and validation of the Counseling Self-Estimate Inventory . Journal of Counseling Psychology , 39 , 105 – 120 .[Crossref], [Web of Science ®] , [Google Scholar]). Both global counselor self-efficacy measures as well as specific measures related to five areas of counseling (i.e., micro skills, process, handling difficult client behaviors, cultural competence, and awareness of values) were analyzed. A total of 20 first-year MA students in counseling psychology completed the COSE at two time intervals 8 weeks apart. Results show a significant increase in the overall measure of self-efficacy skills. We also found a significant increase in one of the factors, micro skills. Implications for training and supervision are discussed.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".