Medication use for chronic health conditions by pregnant women attending an Australian maternity hospital
Bibliographic record
Abstract
BACKGROUND: Most women use medications at some stage in their pregnancy. Medication nonadherence during pregnancy could be detrimental to both mother and fetus. AIMS: To study the extent and nature of the use of prescribed medications during pregnancy and factors associated with medication nonadherence. METHODS: All women≥18 years presenting for their 36th week antenatal visit at the pregnancy clinic of a maternity hospital were invited to complete an anonymous questionnaire that contained 61 items, including the Morisky scale. Factors associated with nonadherence were identified in univariate analysis; factors with P<0.1 were further analysed in a binary logistic regression model. RESULTS: The participants (n=819) had a mean age of 30.8±5.3 years. Most participants were born in Australia, lived with a partner, had university education, were nulliparous, carried one fetus and were nonsmokers. Of these participants, 322 (39.3%) reported a chronic health condition during pregnancy, the most common being asthma (104; 12.7%). Two hundred and seventeen (26.5%) were using prescribed medications, which included anti-anaemics (68; 8.3%), medicines for chronic airway conditions (64; 7.8%), vitamins and minerals (59; 7.2%) and anti-diabetics (43; 5.2%). Nonadherence was reported by 107 (59.1%) participants, mainly because of forgetting (79; 43.6%). Factors associated with nonadherence were having asthma (OR 0.26 (95% CI 0.095-0.72), P=0.009) and using nonprescription dietary minerals (0.30 (0.10-0.87), P=0.027). CONCLUSIONS: Adherence to prescribed medicines during pregnancy is alarmingly low. Health professionals should be more proactive in promoting adherence and assisting women avoid potential fetal harm because of nonadherence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".