Les facteurs sociaux liés au degré d'information de la population du Saguenay‐Lac‐Saint‐Jean à propos des maladies héréditaires et des services disponibles*
Bibliographic record
Abstract
The past few years have seen significant advances in medical genetics. These advances have in turn given rise to several important clinical applications that have targeted populations with specific historical and demogenetic characteristics. One such population lives in the Saguenay‐Lac‐Saint‐Jean region of Quebec. One might think that this population is reasonably well informed of the situation. But their knowledge of population‐specific genetic pathologies and of the services available to them happens to vary considerably from one social group to another. This article presents the results of a quantitative study aimed at determining the factors that contribute to the acquisition of such knowledge in this population. Depuis quelques années, nous assistons à d'importantes avancées en génétique médicale. Celles‐ci induisent un développement sans précèdent de nouvelles applications cliniques qui interpellent certaines populations dont les caractéristiques historiques et démogénétiques font qu'elles sont davantage concernées. C'est le cas, notamment de la population du Saguenay‐Lac‐Saint‐Jean (Québec). On pourrait penser que cette population s'avère, dans l'ensemble, largement sensibilisée mais il appert que le niveau d'information sur les génopathies régionales et les services offerts varie considérablement d'un groupe social à l'autre. Le présent article livre les résultats d'une enquête quantitative visant à connaître les facteurs qui favorisent l'acquisition de telles connaissances dans cette population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".