Medication use for chronic health conditions by pregnant women attending an Australian maternity hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it