THE DESIGN AND EVALUATION OF A KNOWLEDGE TRANSLATION TOOL FOR PREGNANT SOUTH ASIANS AND THEIR PRIMARY CARE PHYSICIANS: USING A SCALABLE APPROACH TO ADDRESS A PUBLIC HEALTH CHALLENGE IN A PRIORITY POPULATION
Notice bibliographique
Résumé
This study, which is focused on addressing the rising prevalence of gestational diabetes mellitus (GDM) in South Asians begins from the perspective that the development of diabetes has scope across public health and anthropology. The onset and progression are rooted within social determinants of health and cultural practices. Similarly, pregnancy—which is a crucial component of the life course—is a time where not only nutrients are shared between mother and child, but also when knowledge is exchanged, and cultural ways are imparted to the pregnant person from their friends and family. Within the South Asian community of Southern Ontario, recent public health evidence demonstrates a high rate of GDM where 1 in 3 South Asians will develop the condition. Babies born to GDM mothers are of higher birthweight and percent body fat than those of non-GDM mothers. Interventions to prevent GDM are important because GDM itself is a risk factor for postpartum obesity, diabetes, and atherosclerosis in the mother, and also because infants with more adipose tissue are more likely to become insulin resistant in adolescence and develop diabetes and cardiovascular disease as adults. Discussions to strengthen the public health response to this challenge can incorporate evidence-based counselling tools (e.g., easily scalable knowledge translation (KT) tools) that can be used by prenatal clinicians providing primary care. Given that diet and physical activity can be influenced not only by an individual locus of control, but also by familial interactions/networks and cultural/traditional foods and expectations, there is a need to better understand and weave in these experiences. I sought to better understand 1) the prenatal lifestyle counselling experiences of South Asians and their family doctors; and 2) the KT tools that have been designed and used in this population; then I used these learnings to develop and evaluate a conceptually-informed, evidence-based KT tool for pregnant South Asians and their family physicians. This dissertation begins with an introduction of patient and provider experiences with lifestyle change. I then present a systematic review and narrative synthesis of prenatal KT tools designed for South Asians. This is followed by a case report that outlines the process taken to develop a patient-facing and provider-facing KT tool (‘SMART START’). Next, I include the design and evaluation of a mixed methods pilot evaluation study of ‘SMART START.’ Finally, I culminate with an epilogue that ties in lessons learned and challenges that were overcome throughout the conduct of this work. The concluding chapter also includes a link to a video that captures the story behind this dissertation and the documentation of how all the aforementioned pieces are nested within and built upon one another.
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 | 0,086 | 0,130 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,003 |
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 ».