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Record W2799477269 · doi:10.1093/workar/way004

Are There Differences in Workplace Accommodation Needs, Use and Unmet Needs Among Older Workers With Arthritis, Diabetes and No Chronic Conditions? Examining the Role of Health and Work Context

2018· article· en· W2799477269 on OpenAlexafffund
Monique A. M. Gignac, Vicki L. Kristman, Peter Smith, Dorcas Beaton, Elizabeth M. Badley, Selahadin Ibrahim, Cameron Mustard

Bibliographic record

VenueWork Aging and Retirement · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsToronto Western HospitalInstitute for Work & HealthSt. Michael's HospitalLakehead UniversityPublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAccommodationContext (archaeology)MedicineGerontologyPsychology

Abstract

fetched live from OpenAlex

= 538). They were recruited from a national panel of 80,000 individuals and a cross-sectional survey was administered online or by telephone. Questionnaires assessed demographics, health, work context, workplace accommodations, and job outcomes. Chi-square analyses, analyses of variance, and regression analyses compared groups. Respondents were similar in many demographic and work context factors. As expected, workers with arthritis and/or diabetes often reported poorer health and employment outcomes. Yet, there were few differences across health conditions in need for or use of accommodations with most participants reporting accommodations needs met. In keeping with work functioning theory, unmet accommodation needs were largely related to work context, not health. Workers whose accommodation needs were exceeded reported better job outcomes than those with accommodation needs met. Findings highlight both work context and health in understanding workplace accommodations and suggest that many older workers can meet accommodation needs with existing workplace practices. However, additional research aimed at workplace support and the timing of accommodation use is needed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.078
GPT teacher head0.319
Teacher spread0.241 · 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

Citations39
Published2018
Admission routes2
Has abstractyes

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