Coupler les données d’enquête de santé aux données de l'assurance maladie : avantages et opportunités pour la recherche en santé publique
Bibliographic record
Abstract
Population-based surveys such as national health interview surveys (HISs) are essential tools to collect information on health status, use of health care and health determinants in the general population.However, the validity of self-reported information through surveys is a concern due to the associated selection and reporting biases.In addition to these validity issues (as a result of selection and reporting bias), HISs are also facing other challenges due to the increasing need of researchers for more comprehensive data to answer complex research questions.Increasing the number of questions in HIS may result in a high workload for interviewers and a significant burden on respondents.This would lead to dropouts resulting in missing data and lower response rates, which both affect data quality.Data linkage has become a popular approach in the secondary use of existing data.address and demographic characteristics of patients.Discharge Abstract Database (DAD), Canada https://www.cihi.ca/en/discharge-abstract-database-metadata-dadMedical records data Primary care data.The data set includes demographic information, date of death, age of deceased, cause of death, occupation of deceased and the health authority to which a person is registered.Intego data, Flanders (Belgium) https://www.intego.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".