Gestational diabetes and barriers to care that may be addressed by physician assistants
Bibliographic record
Abstract
Background – Gestational diabetes mellitus (GDM) is an increasingly common pregnancy complication. It occurs when women not previously diagnosed with diabetes mellitus develop high blood sugar during their pregnancy, typically between 24 to 28 weeks’ gestation. (1) Appropriate management is required to minimize maternal and neonatal adverse outcomes, therefore attending prenatal appointments is crucial. This reality can cause stress and anxiety for mothers who are faced with barriers that do not allow them access to resources to attend their prenatal appointments. (2) Objective – The aim of this paper is an in-depth literature review on the overall topic of GDM and to determine the barriers women face with receiving appropriate management. Furthermore, to determine if there is a justifiable role for physician assistants (PAs) to assist in overcoming these barriers. Methods – An in-depth narrative literature review was undertaken using PubMed and Scopus databases. Keywords used include “gestational diabetes” “barriers or obstacles” and “physician assistants.” The search was limited to the last 15 years and limited to studies completed in North America. Literature Review – GDM women living in a Canadian rural community expressed the barriers they faced when attempting to receive prenatal care. This includes a lack of resources: transportation, child care options, and communication with health care providers. Studies compared a PA and nurse practitioner (NP) role to that of a physician when managing diabetes mellitus and found similar outcomes in control of glycated hemoglobin level (HBA1c), systolic blood pressure (SBP), and low-density lipoprotein cholesterol (LDL-C). Similar outcomes were also found in more complex patients requiring a specialists’ involvement. PAs saw an increased proportion of patients presenting with new complaints, and PAs/NPs combined saw more patients in rural settings than physicians. Conclusion – PAs are caring for patients with similar characteristics and complexity levels as those seen by physicians with no significant clinical difference in patient outcomes. Furthermore, PAs are caring for a higher percentage of patients in rural settings which is where resources are frequently limited. Based on this data, rural Manitoba communities may benefit by integrating a PA that is dedicated to caring for women with GDM.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".