Addressing maternal medication use during breastfeeding using clinical resources and a novel physiologically based pharmacokinetic model-derived metric: A qualitative study
Notice bibliographique
Résumé
Introduction: Breastfeeding has major benefits to the maternal-infant dyad and yet healthcare providers have expressed uncertainty about advocating breastfeeding when mothers are taking medications. The tendency for some providers to be more cautious in their advising approach is likely a consequence of limited, unfamiliar, and unreliable existing information on medication use during lactation. A novel risk metric termed the Upper Area Under the Curve Ratio (UAR) was developed to overcome existing resource shortcomings. However, the perception and use of the UAR in practice by providers is not known. The aim of this study was to understand existing resource use and potential UAR use in practice, their advantages and disadvantages, and areas of improvement for the UAR. Methods: Healthcare providers mainly practicing in California with experience advising on medication use during lactation were recruited. One-on-one semi-structured interviews that included questions on current practices when advising medication use during breastfeeding, and approaches to a given a scenario with and without information about the UAR were conducted. The Framework Method was applied for data analysis to construct themes and codes. Results: Twenty-eight providers representing multiple professions and disciplines were interviewed. Six main themes emerged: (1) Current Practice Approaches, (2) Advantages of Existing Resources, (3) Disadvantages of Existing Resources, (4) Advantages of the UAR, (5) Disadvantages of the UAR, and (6) Strategies to Improve the UAR. Overall, 108 codes were identified that illustrated theme topics ranging from a general lack of metric use to the realities of advising. A workflow describing current practice approaches connected all other themes. Almost all disadvantages of existing resources could be overcome by advantages of other resources and the UAR. Several improvements to the UAR were identified to address its shortcomings. Conclusion: Through interviews with providers who use resources to advise on medication use during breastfeeding, an improved understanding of current practice approaches and accessed resources was ascertained. Ultimately, it was found that the UAR would confer multiple benefits over existing resources, and improvements of the UAR were identified. Future work should focus on implementing the suggested recommendations to ensure optimal uptake of the UAR to improve advising practices.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».