Three Essays on Health and Health Behaviours of Immigrants
Notice bibliographique
Résumé
This thesis focuses on the comparison between immigrants and non-immigrants with respect to various health-related behaviours perspective/viewpoints. Specifically, this thesis comprises three essays. First, I investigate any differences in the factors for utilizing general practitioners (GP) and specialists (SP) between immigrants and non-immigrants in Canada. Second, I examine the causal effects of language proficiency on the health and health behaviours of immigrants to Canada. Finally, I investigate whether there are any differences in the claiming patterns of the Medical Expense Tax Credit (METC) and/or Medical Expense Supplement (MES) for immigrants compared to non-immigrants in Canada. Chapter 1 investigates any differences in healthcare utilization patterns between immigrants and non-immigrants. We implement a two-part model, where the first part applies logistic regressions to assess factors associated with visiting a physician, and the second applies zero-truncated negative binomial regression models to capture the frequency of using healthcare services, conditional on having at least one visit. Our results show that the patterns of healthcare utilization are different for immigrants compared to non-immigrants; differences are also observed by gender and age. More specifically, prescription drug insurance coverage and chronic conditions play opposing roles for male and female immigrants compared to their non-immigrants counterparts. Moreover, the number of years since migration is an important factor in increasing the probability of any general practitioner (GP) and specialist (SP) visit for all immigrants. Chapter 2 is to my knowledge, the first research on the causal effects of language proficiency on health outcomes and healthcare utilization of immigrants in Canada. My finding contradicts the idea that immigrants with poor language facilities are less likely to have a regular doctor. I find that good self-reported health is positively associated with language proficiency. However, I find no statistically significant causal effect of language proficiency on reporting ‘good mental health’. In addition, I find strong evidence that the utilization of hospital and mental health care services are positively associated with being English-language proficient even after controlling for many possible sets of factors. Chapter 3 contributes by supporting existing literature, but with a completely different dimension: the medical tax perspective. I am unaware of any previous research that directly compares the claim patterns of the Medical Expense Tax Credit (METC) and/or refundable Medical Expense Supplement (MES) for immigrants with those of non-immigrants in Canada. My results show that there are differences in the proportions of tax filers who claimed the METC and/or MES, and the amounts of a claim for the METC and/or MES for immigrants compared to non-immigrants; differences are also observed by age, years since migration (YSM), province and immigration categories. In both couples and single families, a lower proportion of immigrant tax filers claimed gross immediate family medical expenses (GME), potential METC claims, and METC refunds compared to non-immigrants. In the case of single families, a higher proportion of non-immigrant tax filers claimed MES compared to their immigrant counterparts.
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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 | 0,003 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».