Sante et travail dans la famille : les donnees probantes liees au diagnostic de cancer d?un conjoint
Bibliographic record
Abstract
Les variations de l?etat de sante influent non seulement sur les personnes qui vivent les changements, mais aussi sur les membres de leur famille. Par exemple, si le membre de la famille qui gagne le revenu principal perd la capacite de travailler en raison d?un choc lie a la sante, la situation aura necessairement une incidence sur la situation financiere du conjoint et des autres personnes a charge. En outre, les conjoints et les enfants en age de travailler peuvent eux aussi accroitre ou reduire leur offre de main-d??uvre afin de compenser la perte de revenu ( ) ou de prendre soin d?un membre de la famille qui est malade ( ). Comme l?etalement de la consommation et l?auto-assurance se produisent au niveau du menage, les effets financiers d?un choc lie a la sante sur les autres membres de la famille ont d?importantes repercussions sur le plan des politiques. Afin de mieux comprendre ces effets, la presente etude examine la facon dont un diagnostic de cancer recu par un conjoint influe sur l?emploi et les gains de l?autre conjoint ainsi que sur le revenu total de la famille (avant impots), en utilisant des donnees administratives canadiennes.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".