Interdisciplinarity in social work education and training in Hungary
Bibliographic record
Abstract
The study analyzes the educational manifestations of the interdisciplinary and interprofessional training of social work in Hungary through a questionnaire used earlier in the USA, Canada and Israel and which is employed simultaneously in Hong Kong and Japan. After a short description of interdisciplinarity and the history of Hungarian social work training, the methodology of the Hungarian research is presented. The analysis of the Hungarian results is followed by their comparison with the available international data. While in most Hungarian educational institutions there is no dual degree program for social workers, about two-thirds of their international counterparts provide such training. The possibilities for interdisciplinary collaborations in field placements were similarly evaluated by the respondents of all nationalities. Research was considered to be the most applicable category for interdisciplinarity by Hungarians as well as for North Americans and Israelis. While respondents in USA, Canada and Israel found community-based research and evaluation most suitable for the improvement of interdisciplinarity in the future, in Hungary this possibility was ranked only fourth after the improvement of course content, field education and informal lectures (‘brown-bags’). As a closure, the authors enlist some interdisciplinary considerations to be taken into account in planning the future of social work education in Hungary.
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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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".