Getting a “SMART START” to gestational diabetes mellitus education: a mixed-methods pilot evaluation of a knowledge translation tool in primary care
Bibliographic record
Abstract
BACKGROUND: South Asian people living in Canada face higher rates of gestational diabetes mellitus (GDM) compared to national trends. The objective of this study was to design and pilot test a knowledge translation (KT) tool to support GDM prevention counselling in primary care. METHODS: This study is a mixed-methods pilot evaluation of the "SMART START" KT tool involving 2 family physicians in separate practices and 20 pregnant South Asians in Ontario, Canada. We conducted the quantitative and qualitative components in parallel, developing a joint display to illustrate the converging and diverging elements. RESULTS: Between January and July 2020, 20 South Asian pregnant people were enrolled in this study. A high level of acceptability was received from patients and practitioners for timing, content, format, language, and interest in the interventions delivered. Quantitative findings revealed gaps in patient knowledge and behaviour in the following areas: GDM risk factors, the impact of GDM on the unborn baby, weight gain recommendations, diet, physical activity practices, and tracking of weight gain. From the qualitative component, we found that physicians valued and were keen to engage in GDM prevention counselling. Patients also expressed personal perceptions of healthy active living during pregnancy, experiences, and preferences with gathering and searching for information, and key preventative behaviours. CONCLUSIONS: Building on this knowledge can contribute to the design and implementation of other research opportunities or test new hypotheses as they relate to GDM prevention among South Asian communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".