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Record W2762670863 · doi:10.1080/1359432x.2017.1387536

Disability and employment – overview and highlights

2017· article· en· W2762670863 on OpenAlexaff
Katharina Vornholt, Patrizia Villotti, Beate Muschalla, Jana F. Bauer, Adrienne Colella, Fred Zijlstra, Gemma M. C. van Ruitenbeek, Sjir Uitdewilligen, Marc Corbière

Bibliographic record

VenueEuropean Journal of Work and Organizational Psychology · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsInstitut universitaire en santé mentale de MontréalInstitut Universitaire en Santé Mentale de QuébecUniversité du Québec à Montréal
FundersUniversiteit Maastricht
KeywordsWorkforceWork (physics)Medical model of disabilityResource (disambiguation)PsychologyMental healthInternational Classification of Functioning, Disability and HealthPopulationWorking populationPublic relationsPolitical scienceGerontologyEconomic growthSociologyMedicinePsychiatryEconomicsEnvironmental health

Abstract

fetched live from OpenAlex

Due to the expected decline in the working-age population, especially in European countries, people with disabilities are now more often recognized as a valuable resource in the workforce and research into disability and employment is more important than ever. This paper outlines the state of affairs of research on disability and employment. We thereby focus on one particular group of people with disabilities, that is to say people with mental disabilities. We define disability according to the International Classification of Functioning, Disability and Health (ICF) of the World Health Organization, by that recognizing that disability results from the interaction of person and environment. Key issues, including the complexity of defining disability, the legal situation in Europe and North America concerning disability at work, and barriers and enablers to employment, are discussed. For each of the topics we show important findings in the existing literature and indicate where more in-depth research is needed. We finalize with a concrete research agenda on disability and employment and provide recommendations for practice.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0020.002
Scholarly communication0.0030.006
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.002

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 head0.076
GPT teacher head0.412
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations400
Published2017
Admission routes1
Has abstractyes

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Same venueEuropean Journal of Work and Organizational PsychologySame topicEmployment and Welfare StudiesFrench-language works237,207