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Record W2098791456 · doi:10.1093/jnci/djn028

Wage Losses in the Year After Breast Cancer: Extent and Determinants Among Canadian Women

2008· article· en· W2098791456 on OpenAlexaffabout
Sophie Lauzier, Elizabeth Maunsell, Mélanie Drolet, Doug Coyle, Nicole Hébert‐Croteau, Jacques Brisson, Benoı̂t Mâsse, Belkacem Abdous, A. Robidoux, J Robert

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversité LavalCentre Hospitalier de l’Université de MontréalInstitut National de Santé Publique du QuébecCentre hospitalier universitaire de QuébecHôpital du Saint-SacrementUniversity of Ottawa
Fundersnot available
KeywordsBreast cancerWageMedicineDemographic economicsDemographyCancerEconomicsLabour economicsInternal medicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Wage losses after breast cancer may result in considerable financial burden. Their assessment is made more urgent because more women now participate in the workforce and because breast cancer is managed using multiple treatment modalities that could lead to long work absences. We evaluated wage losses, their determinants, and the associations between wage losses and changes for the worse in the family's financial situation among Canadian women over the first 12 months after diagnosis of early breast cancer. METHODS: We conducted a prospective cohort study among women with breast cancer from eight hospitals throughout the province of Quebec. Information that permitted the calculation of wage losses and information on potential determinants of wage losses were collected by three pretested telephone interviews conducted over the year following the start of treatment. Information on medical characteristics was obtained from medical records. The main outcome was the proportion of annual wages lost because of breast cancer. Multivariable analysis of variance using the general linear model was used to identify personal, medical, and employment characteristics associated with the proportion of wages lost. All statistical tests were two-sided. RESULTS: Among 962 eligible breast cancer patients, 800 completed all three interviews. Of these, 459 had a paying job during the month before diagnosis. On average, these working women lost 27% of their projected usual annual wages (median = 19%) after compensation received had been taken into account. Multivariable analysis showed that a higher percentage of lost wages was statistically significantly associated with a lower level of education (P(trend) = .0018), living 50 km or more from the hospital where surgery was performed (P = .070), lower social support (P = .012), having invasive disease (P = .086), receipt of chemotherapy (P < .001), self-employment (P < .001), shorter tenure in the job (P(trend) < .001), and part-time work (P < .001). CONCLUSION: Wage losses and their effects on financial situation constitute an important adverse consequence of breast cancer in Canada.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.253
Teacher spread0.218 · 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 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

Citations153
Published2008
Admission routes2
Has abstractyes

Explore more

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