Centering Birthing Experiences of Women of Color: Protocol for a Qualitative Maternal Near Miss Study
Notice bibliographique
Résumé
BACKGROUND: In the United States, Black women are 3-4 times more likely to experience maternal near miss (MNM) or severe maternal morbidity (SMM) than non-Hispanic White women. However, there is a limited narrative-based investigation into Black and other marginalized women's MNM experiences. Additionally, limited extant research on the impact of MNM and SMM on birthing women's families or support persons and health care providers precludes the development of multilevel, patient-centered methods to eliminate these racial or ethnic disparities. OBJECTIVE: This paper presents the protocol for a study that aims to draw insights from the experiences of racially and socioeconomically diverse mothers with MNM and SMM, their family or support persons (eg, partners), and health care providers to inform legislation, clinical practice, and infrastructure for optimal social support using PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) guidelines. Using a storytelling approach to assess participants' risk factors, document underlying causes, and research clinical causes of MNM, researchers hypothesize these data will inform policies to improve maternal conditions and provide safe and effective prevention and treatment options for birthing persons. METHODS: Morehouse School of Medicine (MSM) will partner with health services and community-based organizations to promote inclusive participant recruitment for this multiphase study. In phase 1, qualitative interviews were conducted with birthing women (n≤87) who have experienced MNM and SMM. In phase 2, we will conduct qualitative interviews with the following groups: birthing women's partners or support persons (n≤50), health care providers serving birthing women (n≤50), and adults who lost their mothers to pregnancy-related complications (n≤50). In each phase, the total number of participants interviewed will be based on theoretical saturation, that is, the point in iterative data collection and analysis when all important insights have been exhausted from the data already available. RESULTS: Recruitment for phase 1 started in July 2021. As of March 2024, we have recruited 87 racially and socioeconomically diverse birthing women. Of those, 74% (64/87) self-identified as Black or African American, 20% (17/87) as Hispanic or Latina, and 9% (8/87) as Native American or Alaska Native. Severe preeclampsia accounted for 46% (40/87) of participants' pregnancy-related adverse experiences. Qualitative interviews grounded in narrative-based medicine are ongoing. Recruitment for phase 2 will occur between July 2023 and December 2024. Study results will be published in peer-reviewed scientific journals. CONCLUSIONS: The findings from this research will deepen the understanding of how severe obstetric complications (1) are experienced by birthing women; (2) are perceived by their partners, support persons, and health providers; and (3) impact the lives of bereaved family and community members.
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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 | 0,072 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,009 | 0,005 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,056 | 0,009 |
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 ».