An investigation of psycho-social factors associated with the uptake of pre-pregnancy care in Australian women with type 1 and type 2 diabetes
Bibliographic record
Abstract
Pre-pregnancy care (PPC) reduces adverse pregnancy outcomes for women with pre-existing diabetes. Yet, despite the compelling case for PPC, participation rates remain poor. The reasons for poor participation are as yet unclear. The aim of this study was to further our understanding of the factors-associated PPC uptake, particularly attitudes and beliefs towards PPC using models of health behaviour: The Health Belief Model, Social Cognitive Theory, and Theory of Reasoned Action. Participants comprised 123 women with type 1 and 2 diabetes attending outpatient clinics for diabetes and pregnancy, who completed questionnaires. Logistic regression analysis indicated that after adjusting for socio-demographic factors, exposure to a greater number of cues was a significant predictor of PPC participation (odds ratio [OR]: 1.93; 95% confidence interval [95% CI]: 1.13-3.28). Other significant predictors of PPC uptake were older age (OR: 1.13; 95% CI: 1.01-1.26) and not having children (OR: 3.93; 95% CI: 1.28-12.06). The findings from this study support initiatives to provide cues to PPC for women with diabetes to enhance PPC uptake. Further, some groups such as younger women as well as women with children may possibly be considered for the focus of more vigorous intervention efforts.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".