Latinas Lived Experience of IPV Amidst the COVID-19 Global Pandemic. Los Platos Sucios se Lavan en Casa
Notice bibliographique
Résumé
Background: The largest minority group in the United States is represented by Latinos, with Latinas comprising a significant portion of this demographic. Latinas will account for a quarter of the population living in the U.S. by 2050. Studies have indicated Latinas are at a higher risk of experiencing IPV, and researchers have found about 50% of IPV incidents in this community are grossly underreported. The COVID-19 pandemic profoundly impacted the Latino population, with data from the Centers for Disease Control (CDC) showing Latinos have higher rates of COVID-19-related morbidity and mortality. The pandemic compounded the existing difficulties faced by this community, including financial hardships and obstacles to accessing healthcare and resources. Advocates for IPV expressed concern about COVID-19 mandatory stay-at-home orders and social isolation measures taken to control the spread of the disease may have exacerbated IPV placing the mental and physical health of IPV victims at risk. Purpose: The study aimed to provide a deeper understanding of the impact of COVID-19 on IPV among Latinas. This study used a phenomenological approach to explore the lived experiences of Latinas who experienced IPV during the COVID-19 pandemic. The research objectives included exploring how Latinas describe IPV, examining their lived experiences with IPV during the mandatory lockdown phase, and identifying perceived barriers to accessing IPV resources, medical care, and emergency services during the pandemic. Methods: This study used a phenomenological approach to understand the lived experiences of Latinas who faced IPV during the COVID-19 pandemic, specifically from March 19, 2020, to January 25, 2021. Participants were recruited through purposive and snowball sampling methods, and data were collected through open-ended questions, demographic surveys, and the ACEs questionnaire. The study prioritized participant privacy and comfort, and trustworthiness was ensured using the Lincoln-Guba framework and bracketing. Furthermore, the researcher used the hermeneutic circle to analyze data and establish themes, which involved interesting pieces of data. Finally, process coding was used to analyze the data further and identify common themes among the 12 participants who were interviewed between January 13, 2022, to August 10, 2022. Findings: Four themes were formed (a) beliefs of cultural norms; (b) adverse emotions: feelings of guilt and extreme vulnerability; (c) mistrust in the legal system; and (d) perceiving the COVID-19 response as a barrier to receiving resources for IPV. The themes identified in the study provided a descriptive understanding of the phenomenon, which helped reveal the essence and meaning of the
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,006 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».