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Record W2134369111

The Effects of Cancer on Employment and Earnings of Cancer Survivors

2014· article· en· W2134369111 on OpenAlexaboutno aff
Sung‐Hee Jeon

Bibliographic record

VenueAnalytical Studies Branch Research Paper Series · 2014
Typearticle
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMicrodata (statistics)EarningsDemographyCancer registryWageCancerMatching (statistics)MedicinePropensity score matchingCausal inferenceEconometricsActuarial scienceDemographic economicsEconomicsCensusEnvironmental healthLabour economicsSurgeryPopulationAccountingInternal medicineSociology
DOInot available

Abstract

fetched live from OpenAlex

The study examines the effects of cancer on the work status and annual earnings of cancer survivors who had a strong attachment to the labour market prior to their diagnosis. The comparison group consists of similar workers never diagnosed with cancer. The study is based on a Statistics Canada linkage file that combines microdata from the 1991 Census, the Canadian Cancer Registry, mortality records and personal income tax files. The study estimates changes in the magnitude of cancer effects during the first three years following the year of the diagnosis using a large sample of cancer survivors diagnosed at ages 25 to 61. The empirical strategy combines matching and regression models to deal with observed and unobserved differences between the cancer and comparison samples, and to improve causal inference.

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)0.008
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 score0.099
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.159
GPT teacher head0.504
Teacher spread0.345 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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