Things Latinas need to plan for safety: A dual-site concept mapping study
Bibliographic record
Abstract
BACKGROUND: Latina women in the United States experience intimate partner violence (IPV) at high rates, but evidence suggests Latinas seek help for IPV at lower rates than other communities. Safety planning is an approach that provides those experiencing IPV with concrete actions to increase their safety and referrals to formal services. While safety planning is shown to reduce future incidences of violence, little is known about the safety planning priorities of Latinas. APPROACH: This study leveraged Group Concept Mapping, a mixed-method process consisting of brainstorming, sorting, rating, and interpreting. First, 17 Latinas who were survivors of IPV and/or professional advocates generated responses to a focal prompt. Next, 19 participants pile-sorted a list of unduplicated responses into categories. Data were analyzed using multidimensional scaling and hierarchical cluster analysis and results were iteratively refined by the study team. Forty-two Latina participants in Chicago and Miami rated each item based on its necessity for safety using two scenarios: if a survivor planned to leave a partner or remain in the relationship. Bivariate correlation analyses were used to examine differences in safety planning priorities across multiple axes. Finally, results were shared with participants for feedback and contextualization. RESULTS: Combined, a total of 46 Latinas participated in data collection activities. Brainstorming and sorting data generated seven clusters of safety planning needs. Statistically significant differences in cluster rating were found for women who intended to leave a relationship (for whom legal services was most necessary) versus those who intended to stay (for whom safety planning services were critical) and those in Chicago versus those in Miami. CONCLUSIONS: This study contributes to the small, but growing, literature regarding safety planning needs of Latinas in the US and illustrates potential ways in which future safety planning interventions should be tailored to these communities.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".