Development of the Lesbian, Gay, and Bisexual Affirmative Counseling Self-Efficacy Inventory – Short Form (LGB-CSI-SF).
Bibliographic record
Abstract
The Lesbian, Gay, and Bisexual Affirmative Counseling Self-Efficacy Inventory - Short Form (LGB-CSI-SF) was developed to facilitate LGB-affirmative counseling training, as well as process and outcome research, by offering a brief psychometrically supported version of the original LGB-CSI measure to researchers and clinicians. Five hundred seventy-five participants (435 licensed mental health professionals and 140 graduate students/trainees) constituted the sample. Confirmatory factor analyses of the 32 items from the original LGB-CSI yielded a new 15-item version of the measure composed of 5 factors (consisting of 3 items each) that assess counselor self-efficacy to perform lesbian, gay, and bisexual (LGB) affirmative counseling behaviors (Application of Knowledge, Advocacy Skills, Self-Awareness, Relationship, and Assessment). The LGB-CSI-SF evidenced high internal consistency and adequate test-retest stability. Convergent validity was supported by correlations between LGB-CSI-SF total scores and Application of Knowledge, Advocacy Skills, Relationship, and Assessment subscales and instruction in LGB issues, as well as personal/professional relations with LGB individuals. More affirmative attitudes toward LGB persons positively related with total scores and Advocacy Skills, Self-Awareness, and Relationship subscales. Discriminant validity was evidenced by an absence of relations between LGB-CSI-SF subscales and a measure of impression management. We found no associations between Advocacy Skills, Assessment, and Relationship subscales and a measure of Self-Deception. Recommendations for implementing the LGB-CSI-SF in future LGB-affirmative counseling self-efficacy based research and training interventions 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".