Notice bibliographique
Résumé
This study primarily aims to develop an Agent Based Model (ABM) that can simulate the obesity rates based on statistical analysis and to find out how obesity is affected by risk factors in a Canadian environment. As obesity can have many causes, it is assumed that various risk factors, not just a decisive one, have an influence on obesity and they interact with one another. Therefore, unlike most previous studies, I approached the obesity problem as a Complex -Adaptive System (CAS). The data used for this study was provided by Statistics Canada, and the Canadian Community Health Survey (CCHS). This survey is a cross-sectional survey that collects self-reported information related to health status, health care utilization, and health determinants for the Canadian population. To build the Obesity ABM, it is necessary to find out which risk factors are closely associated with obesity and to what extent they interact with one another. Twelve categories of factors that are expected to influence the obesity rate were chosen on the basis of the related works. Through the statistical data analysis carried out, the main factors and variables for obesity were identified and their respective mathematical relationships obtained. From this, two categories that have several sub-factors for the obesity model were chosen. I implemented statistical data analysis on the CCHS dataset to see the interrelationship among the factors. Also, I implemented a year-to-year analysis that can show how people change their obesity status each year. Based on the data analysis result, I defined rules for how each risk factor changes each year. These rules are applied to the obesity model using NetLogo. The architecture of obesity model implementation consists of three main parts: The population module, the risk factor module, and the results module. Performance evaluation was conducted to examine whether the obesity model can simulate the obesity rate. For this evaluation, the data of CCHS from 2009 to 2014 and the result of the obesity model which is generated by simulation are compared. Model calibration was executed to fit the actual data to the model test result. The result of the model test shows that the percentage error is less than 5%. This means that the obesity model has high validity in predicting obesity for each risk factor. The obesity ABM is a useful tool to find out the risk factors related to obesity and their relationships in the Canadian population. Thus, this model can potentially assist to improve obesity management at various levels. At the individual level, everyone can find what kinds of strategies are best fit to improve her/his physical condition. Also, at a government or community level, it could help develop policies for people to continue to implement these strategies well. This will lead to reducing the associated social costs and help to promote national health.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».