Between Academia and the Field: The Case of School Counselling Effectiveness of School Counselling Training and Its Impact on Professional Identity
Bibliographic record
Abstract
Previous studies confirmed that training for master’s programs for school counseling is deficient. The present study differentiates between three types of academic institutions that train school counselors in Israel: Universities, education colleges and academic colleges, in terms of how professional counselors evaluate the efficiency of academic studies to the school fieldwork and their impact on personal and group professional identity. A total of 158 school counselors, divided into groups of educational institutions, participated in this study. Of whom 5 men and 153 women, aged 27 to 63 years (M = 43.63; SD = 8.11), while the seniority in counseling profession ranged from 1 to 32 years (M = 9.65, SD = 7.60). After factor analysis of the ‘School Counselling Training Efficiency’ questionnaire, a significant difference between the training institutions was found only in the training efficiency, but not in professional identities. Universities-trained counselors reported less training efficiency than counselors trained at education and academic colleges. The average of academic training efficiency was also low in two typical colleges. Training efficiency fully mediated the relationship between training institution and both professional identities. The research findings raised pointed questions about the efficiency of training for school counselors in academia, in all educational institutions. It is important to develop evaluation of school counseling students in their training to support personal and group professional identity.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".