Malattie cardiovascolari e fattori socioeconomici di rischio: un analisi spaziale empirica su Calgary (Canada)
Bibliographic record
Abstract
Les maladies cardio-vasculaires et les facteurs socio-economiques du risque:une analyse spatiale empirique de Calgary (Canada). - Cet article presente une application des modeles spatiaux autoregressifs a l'analyse du lien entre les maladies cardiovasculaires et les facteurs de risque localises de nature demographique et socio-economique. Au-dela de la relation bien connue entre la maladie et les facteurs non-modifiables de risque, tels que l'âge et le genre, les modeles fournissent une evaluation fiable de la relation entre la maladie et les facteurs modifiables de risque, tels que la condition familiale, le niveau d'education et le revenu. L'analyse multivariee permet d'identifier des poches localisees mais pas toujours evidentes de population a risque, fournissant ainsi la base pour la definition de politiques et d'interventions socio-sanitaires adressees aux segments de la population qui sont le plus a risque et visant a limiter la frequence de la maladie. L'emploi de techniques explicitement spatiales permet de reduire effectivement la variabilite des estimations donnees par les modeles, en augmentant leur fiabilite et, par consequent, leur efficacite en tant qu'instruments analytiques pour l'identification de politiques d'intervention efficaces.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".