Assessing a Digital Tool to Screen and Educate Survivors of Domestic Violence on Affordable Housing Programs in New York City: Protocol for a Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Extant research has long documented the association between domestic violence and homelessness. Yet, there appear to be few interventions to address the housing needs of survivors of domestic violence, and none on a digital platform. OBJECTIVE: Our primary objective is to determine the feasibility of a full-scale intervention trial of a web-based tool that screens and educates survivors of domestic violence on affordable housing programs in New York City. Our secondary objectives are to assess the perceived usability and acceptability of the tool. METHODS: The study will take place in a community-based domestic violence center in New York City. Treatment will consist of study participants not using (SC) or using (SC+) the tool, in or outside of private meetings with a case manager to discuss housing and other benefits. The frequency of the meetings will vary depending on the participant's needs. The study will measure changes in housing knowledge, housing self-efficacy, and staff trust through two electronic surveys, administered at times 0 and 2 weeks. Following a historical cohort control group design, we will sequentially recruit participants, starting with SC and followed by SC+. After data collection for SC+ ends, we will invite staff from the partner site to individual, web-based interviews to share their experiences of and recommendations for implementing the tool. RESULTS: Recruitment for the SC arm commenced in March 2022 and was completed in April 2023. After a year, 23 participants completed the study: 75 were screened, 44 were deemed eligible, 35 enrolled, and in the end, 23 participants completed baseline and follow-up surveys. Given the length of time it took to recruit for SC and the limited time overall that we had for the study, the study team decided to follow an expedited recruitment timeline for SC+. Recruitment for SC+ commenced in January 2024 and is anticipated to end by May 2024. Recruitment for the staff interviews will take place in June 2024. We expect to complete the study and be ready to compile the results by the end of June 2024. CONCLUSIONS: The protocol describes a feasibility study that can inform future research on housing or digital tools for a similar study population. Data from the study will also be used to inform revisions to the tool. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63162.
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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 | 0.050 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".