A Social-Ecological Model Exploring Gestational Diabetes Mellitus Screening Practices Among Antenatal Health Care Providers
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is associated with adverse health outcomes for the pregnant individual and their baby. Screening approaches for GDM have undergone several iterations, introducing variability in practice among healthcare providers. As such, our study aimed to explore the views of antenatal providers regarding their practices of, and counseling experiences on the topic of, GDM screening in Ontario. We conducted a qualitative, grounded theory study. The study population included antenatal providers (midwives, family physicians, and obstetricians) practicing in Hamilton, Ottawa, or Sudbury, Ontario. Semi-structured telephone interviews were conducted and transcribed verbatim. Transcripts were analyzed using inductive coding upon which codes, categories, and themes were developed to generate a theory grounded in the data. Twenty-two participants were interviewed. Using the social-ecological theory, we created a model outlining four contextual levels that shaped the experiences of GDM counseling and screening: Intrapersonal factors included beliefs, knowledge, and skills; interpersonal factors characterized the patient-provider interactions; organizational strengths and challenges shaped collaboration and health services infrastructure; and finally, guidelines and policies were identified as systemic barriers to health care access and delivery. A focus on patient-centered care was a guiding principle for all care providers and permeated all four levels of the model. Patient-centered care and close attention to barriers and facilitators across intrapersonal, interpersonal, organizational, and policy domains can minimize the impact of variations in GDM screening guidelines. Among care providers, there is a desire for additional skill development related to GDM counseling, and for national consensus on optimal screening guidelines.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".