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Relationship between Collaboration in Work Support and Knowledge and Skills in Providing Support for Individuals with Disabilities

2021· article· en· W4200522001 on OpenAlexvenueno aff
Kazuaki Maebara, Jun Yaeda

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2021
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsnot available
FundersMinistry of Health, Labour and Welfare
KeywordsWork (physics)Vocational educationPsychologyVocational rehabilitationWelfareMedical educationRehabilitationQuality (philosophy)Test (biology)MedicinePedagogyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Background: There has been growing hope for initiatives supporting the transition of persons with disabilities from employment-related welfare services to employees in companies. This is against the backdrop of a significant increase in employment among persons with disabilities in Japan. Objective: To improve the quality of this transition, this study examined the relationship between the collaboration of the Work Support Centers for Continuous Employment Type B (WSC-B) with vocational rehabilitation organizations (VROs) and knowledge and skills related to employment support. Methods: A survey including all 122 WSC-B in L-Prefecture was conducted by postal mail. The respondents were asked to rate items on the following six levels concerning collaboration with VROs and understanding of VROs. We used the Japanese version of the Self-Assessment for Students or Counselors (SASC-J) to assess knowledge and skills related to work support. Results: The t-test conducted on knowledge and skills status between WSC-B with a high degree of cooperation with VROs and those with low degrees confirmed significant differences among the various subsystems in VROs. Conclusion: Based on our results, we suggest that maximizing the use of collaboration in work support will enhance the support provided by WSC-B and promote transition support for people with disabilities.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.292
Teacher spread0.254 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Intellectual Disability - Diagnosis and TreatmentSame topicErgonomics and Human FactorsFrench-language works237,207