Address Trainee Counselors’ Perplexities: Integrating Predictors of Self-Efficacy into Counseling Ethics Education
Bibliographic record
Abstract
The process of addressing ethical perplexities is challenging and there is insufficiency of clear solutions and proper resources to resolve the ethical quandaries. Thus, this study aims to identify the greatest predictor of trainee counselors’ self-efficacy in addressing ethical perplexities. The greatest predictor of self-efficacy is expected to enhance the existing counseling ethics education curriculum and pedagogy. A quantitative methodological approach was administered through questionnaires. There were 148 trainee counselors selected through simple random cluster sampling and they were all students from private universities in Malaysia. The data gathered were analyzed using correlation and multiple regression analyses. Correlation analysis recorded the highest coefficient of r value which was a substantial relationship between self-efficacy and multicultural competence. Next, multiple regression analyses indicated that the three predictors which are multicultural competence, religiosity, and spirituality were predictive of self-efficacy. However, the strength of prediction varied. Multicultural competence had the most powerful prediction on trainee counselors’ self-efficacy, followed by spirituality, and religiosity. These findings revealed the importance of the three predictors in enhancing trainee counselors’ self-efficacy, addressing ethical quandaries, and developing pedagogic counseling education. The process of preparing and developing intuitive counseling professionals is worthy of attention in the counseling training and practice, specifically in addressing ethical perplexities. Thus, the three predictors should be integrated into the counselor education curriculum and pedagogy at all levels. Cultivation of these three predictors in all trainee counselors would build their level of self-efficacy and help in their survival during counseling dilemmas, leading to successful counseling sessions.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".