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Record W2433027366

Underestimating the value of women: assessing the indirect costs of women with systemic lupus erythematosus. Tri-Nation Study Group.

2000· article· en· W2433027366 on OpenAlexaffabout
A. Clarke, J.R. Penrod, Y. St. Pierre, M. A. Petri, S Manzi, David Isenberg, Caroline Gordon, Jean‐Luc Senécal, Paul R. Fortin, Nurhan Sutcliffe, J. R. Goulet, D. Choquette, T. Grodzicky, John M. Esdaile

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsAbsenteeismMedicineProductivitySplit labor market theoryIndirect costsLabour economicsSecondary labor marketValue (mathematics)Demographic economicsEconomicsLabor relationsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Indirect costs result from diminished productivity and are incorporated in cost-benefit analysis to guide health resource allocation. Valuing the productivity impairment of those not involved in labor market activities is controversial but important for diseases affecting predominantly women if allocation decisions are to be economically efficient and equitable. We compared indirect costs incurred by women with systemic lupus erythematosus (SLE), a prototypical women's disease, calculated under varying assumptions for the value of diminished labor market and non-labor market activity. METHODS: Six hundred forty-eight female patients with SLE reported on employment status and time lost by themselves and their caregivers from labor market and non-labor market activities over a 6 month period. RESULTS: Average annual indirect costs ranged from $1,424 to $22,604 (1997 Canadian dollars) dependent on the value assigned to labor market and non-labor market activity. CONCLUSION: Indirect cost estimates that fail to consider longterm labor market absenteeism and diminished non-labor market productivity and do not use gender neutral wages to value labor market activity may lead to decisions that jeopardize resources for women's diseases.

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)0.008
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 score0.311
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.288
Teacher spread0.255 · 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

Citations54
Published2000
Admission routes2
Has abstractyes

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