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
Bibliographic record
Abstract
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.
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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 | 0.086 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".