Littératie en santé et prévention du cancer
Bibliographic record
Abstract
In recent years, there has been a noticeable drop in mortality rates from cancer, although cancer remains the primary cause of death in France and in the province of Québec. Several factors contribute to this reduction in mortality rates.First, better cancer screening is provided, and better follow ups are offered when abnormalities are detected. Second, cancer treatments benefit from ongoing developments which provide new treatments and more efficient measures to fight this illness. Last, we must also credit promotional campaigns to adopt healthy habits and lifestyles, particularly the fight against smoking.However, cancer strikes preferentially in some subgroups. In particular, cancer rates are higher and cancer-screening rates are lower in some subgroups, increasing disparities amongst subgroups of the same population. It seems that an insufficient level of literacy could be a factor explaining these discrepancies.This article presents a brief definition of the concept of literacy in general, followed by a definition of health-literacy behaviors and competencies. Then, we will present some data from research and from literature reviews on the potential linkages between literacy and cancer in general, and specific cancers in particular. We will conclude by considering a path to literacy in cancer screening.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".