Community pharmacist counseling in early pregnancy—Results from the SafeStart feasibility study
Bibliographic record
Abstract
BACKGROUND: Community pharmacists are available to counsel women in early pregnancy, but no studies have assessed the feasibility of such a service. OBJECTIVE: To test the feasibility of a pharmacist consultation in early pregnancy and to inform the design of a definitive trial. SETTING: Six community pharmacies in Norway from Oct. to Dec. 2017. METHOD: We evaluated recruitment approaches and an automatic data preprocessing system (ADPS) to enroll, assign participants, and distribute questionnaires. Women (≥18 years) in early pregnancy were eligible for inclusion. Participants were assigned to a pharmacist consultation (intervention group) or standard care (control group). The intervention aimed to address each woman's concerns and needs regarding medications and ailments in pregnancy, and was documented on a standard form. The women's acceptability of the intervention was measured by a questionnaire. MAIN OUTCOME MEASURES: Appropriate recruitment approaches, workflow of the ADPS, and women's acceptability of the intervention. RESULTS: Of the 35 participants recruited, 19 were recruited through Facebook. The ADPS worked well. Treatment of nausea and vomiting (NVP) (10/11) and general information about medications (8/11) were frequently discussed during the consultations (n = 11). The women reported high satisfaction with the consultation. Having the option of telephone and follow-up consultations was important to the women. CONCLUSION: It is feasible to provide community pharmacist consultations in early pregnancy. In a definitive study, the consultations should focus on NVP and general medication use and further explore social media as a recruiting tool. Both in-pharmacy and telephone consultations should be offered to deliver the intervention.
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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 | 0.019 | 0.031 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".