The effect of a pharmacist consultation on pregnant women’s quality of life with a special focus on nausea and vomiting: an intervention study
Bibliographic record
Abstract
BACKGROUND: Maternal wellbeing and quality of life (QOL) are increasingly being recognized as important for healthy pregnancies. The aim of this study was to investigate the impact of a pharmacist consultation on pregnant women's QOL focusing on nausea and vomiting in pregnancy (NVP), and patient satisfaction. METHODS: For this intervention study in 14 community pharmacies, women in early pregnancy were recruited and assigned to a pharmacist consultation (intervention) or standard care (control). The consultation aimed to address each woman's concerns regarding medications and pregnancy-related ailments. Data were collected through online questionnaires at baseline (Q1) and during the second trimester (Q2). The intervention group completed an additional satisfaction questionnaire after the consultation was completed. The primary outcome was the impact of the intervention on the Quality of Life Scale (QOLS) scores between the first and second trimesters. The impact of the intervention was assessed by linear regression, and secondary analyses were performed to assess effect modification by NVP. RESULTS: Of the 340 women enrolled in the study, we analyzed data for 245. Half (170/340) of the original participants were allocated to the intervention group, of whom 131 received the pharmacist consultation. Most women (75%, 78/96) reported that the consultation was useful to a large/very large extent. The consultation had no overall impact on QOLS scores between the first and the second trimesters compared with standard care (adjusted β: 0.7, 95% CI: -2.1, 3.4). The impact of the intervention on QOLS was greater amongst women with moderate/severe NVP (adjusted β: 3.6, 95% CI: -0.6, 7.7) compared to those with no/mild NVP (adjusted β: -1.4, 95% CI: -5.1, 2.2) (interaction term study group*NVP severity, p = 0.048). CONCLUSIONS: The pregnant women highly appreciated the pharmacist consultation, but the intervention did not affect their QOL scores compared with standard care. Future studies should further explore the effect of a pharmacist consultation specifically for NVP and on other outcomes such as use of health care services and medication use in pregnancy. TRIAL REGISTRATION: Retrospectively registered in ClinicalTrials.gov (identifier: NCT04182750 , registration date: December 2, 2019).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".