Self-efficacy of Guidance Counselors in the use of eclectic interventions in counseling
Bibliographic record
Abstract
This study explored the perspectives and challenges Registered Guidance Counselors (RGCs) faced with eclectic counseling.Among the CNSC RGCs, there was a wide range of knowledge, attitudes, and practical experiences, often shaped by each counselor's unique background and formal training.RGCs highlighted adaptability and individualized care as key, finding that no single therapeutic model adequately addressed the diversity of client issues.Over time, they shifted from relying on a single approach to integrating techniques tailored to individual needs, enhancing client empowerment and ensuring safety in urgent cases.They cited client engagement as essential to successful counseling and emphasized the counselor's role in maintaining high standards, ongoing education, and ethical conduct.On the other hand, through a review of 10 studies, key trends emerged: flexibility, e-counseling, youth-oriented therapy, integrative evaluation, and the impact of counselor attitudes on intervention choices.These trends underscored a growing shift toward flexible and client-centered eclectic approaches in counseling.Most RGCs felt confident in designing and implementing eclectic methods, with 80% expressing high self-efficacy in tailoring techniques to clients.This self-efficacy positioned RGCs well to address complex client needs in the dynamic mental health environment.
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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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".