Income and education inequalities in cervical cancer incidence in Canada, 1992–2010
Bibliographic record
Abstract
BACKGROUND: There is evidence of socioeconomic inequalities in cancer incidence in Canada and other countries globally, yet there is no study investigating socioeconomic inequalities in national cervical cancer incidence in Canada. Thus, the current study investigated income and education inequalities in the incidence of cervical cancer in Canada from 1992 to 2010. METHODS: Data were derived from a linked dataset that combined cervical cancer incidence from the Canadian Cancer Registry and demographic and socioeconomic information from the Canadian Census of Population and the National Household Survey. The Concentration index approach was used to measure income and education inequalities in the incidence of cervical cancer over time. RESULTS: National incidence of cervical cancer decreased significantly from 1992 to 2010. The age-standardized C was negative for the majority of years for both income and education inequalities, but the preponderance were not significant. Trend analyses of socioeconomic inequalities suggested an increasing concentration of cervical cancer incidence among less-educated females over the study period. CONCLUSIONS: Over almost two decades, there were no pervasive socioeconomic inequalities in the incidence of cervical cancer in Canada. As such, policies aimed at reducing the incidence of cervical cancer should focus on the general population, irrespective of socioeconomic status.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".