Attending to Power and Diversity in Supervision: An Exploration of Supervisee Learning Outcomes and Satisfaction With Supervision
Bibliographic record
Abstract
Diversity is becoming more important in clinical training programs as clients, students, and supervisors become more diverse. At the same time there is a focus on learning outcomes in order for programs to graduate competent therapists. Supervision that attends to power and diversity can provide a supportive environment where the supervisor can model the importance of addressing these issues to influence (a) positive clinical outcomes for clients because of isomorphism, (b) satisfaction with supervision, and (c) enhanced learning outcomes for supervisees. The purpose of this study was to explore the use and influence of feminist supervisory practices on satisfaction and learning outcomes from the perspective of supervisees and supervisors in Commission on Accreditation for Marriage and Family Therapy Education-accredited programs who completed a 70-question online survey. Results indicated that from supervisees' perspective attending to power and diversity in supervision influenced satisfaction with supervision (β = .793, p < .001) and learning outcomes (β = .806, p < .001). From supervisors' perspective there were no significant effects of attending to power and diversity in clinical supervision on supervisor satisfaction with supervision or supervisee learning 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".