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Record W1501800503 · doi:10.1002/art.20822

Managing arthritis and employment: Making arthritis‐related work changes as a means of adaptation

2004· article· en· W1501800503 on OpenAlexaff
Monique A. M. Gignac, Elizabeth M. Badley, Diane Lacaille, Cheryl C. Cott, Paul Adam, Aslam H. Anis

Bibliographic record

VenueArthritis Care & Research · 2004
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of British ColumbiaVancouver Hospital and Health Sciences CentreToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsPsychosocialMedicineArthritisRheumatoid arthritisWork (physics)Adaptation (eye)PerceptionPhysical therapyClinical psychologyPsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To understand arthritis-related workplace changes, including occasional work loss and changes to the type and hours of work, and the factors associated with them using theories of adaptation and behavior change as a framework. METHODS: Participants were 492 employed individuals with rheumatoid arthritis or osteoarthritis. They completed an interview-administered, structured questionnaire assessing demographic, workplace, and psychosocial variables, as well as such work transitions as changes to the hours, type, and nature of work. Hypotheses were examined using multiple linear regression. RESULTS: Seventy percent of respondents made at least 1 work change. Younger participants and those with greater workplace activity limitations reported more changes. Work changes were associated with greater depression. A hypothesized 3-way interaction among people's perceptions of their capacity, their future job expectations, and whether they had told their employer about their arthritis was significant. CONCLUSION: This study extends arthritis employment research by examining a range of work changes. It highlights the dynamic interplay among arthritis, workplace, and psychosocial variables to understand adaptation to arthritis disability.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.333
Teacher spread0.296 · 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 designOther design
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

Citations136
Published2004
Admission routes1
Has abstractyes

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