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Record W2081317098 · doi:10.1177/0020872811427717

Interdisciplinarity in social work education and training in Hungary

2011· article· en· W2081317098 on OpenAlexaboutno aff
Péter Török, Yossi Korazim-Kὅrösy

Bibliographic record

VenueInternational Social Work · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Training (meteorology)Social workSociologyMedical educationSocial sciencePolitical sciencePedagogyMedicineGeographyEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.083
GPT teacher head0.406
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2011
Admission routes1
Has abstractyes

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