Latinas Lived Experience of IPV Amidst the COVID-19 Global Pandemic. Los Platos Sucios se Lavan en Casa
Bibliographic record
Abstract
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".