Designing an Information and Communications Technology Tool With and for Victims of Violence and Their Case Managers in San Francisco: Human-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: Violence is a public health problem. Hospital-based violence intervention programs such as the San Francisco Wraparound Project (WAP) have been shown to reduce future violent injury. The WAP model employs culturally competent case managers who recruit and enroll violently injured patients as clients. Client acceptance of the WAP intervention is variable, and program success depends on streamlined, timely communication and access to resources. High rates of smartphone usage in populations who are at risk for violent reinjury create an opportunity to design a tailored information and communications technology (ICT) tool to support hospital-based violence intervention programs. OBJECTIVE: Current evidence shows that ICT tools developed in the health care space may not be successful in engaging vulnerable populations. The goal of this study was to use human-centered design methodology to identify the unique communication needs of the clients and case managers at WAP to design a mobile ICT. METHODS: We conducted 15 semi-structured interviews with users: clients, their friends and families, case managers, and other stakeholders in violence intervention and prevention. We used a human-centered design and general inductive approach to thematic analysis to identify themes in the qualitative data, which were extrapolated to insight statements and then reframed into design opportunities. Wireframes of potential mobile ICT app screens were developed to depict these opportunities. RESULTS: Thematic analysis revealed four main insights that were characterized by the opposing needs of our users. (1) A successful relationship is both professional and personal. Clients need this around the clock, but case managers can only support this while on the clock. (2) Communications need to feel personal, but they do not always need to be personalized. (3) Healing is a journey of skill development and lifestyle changes that must be acknowledged, monitored, and rewarded. (4) Social networks need to provide peer support for healing rather than peer pressure to propagate violence. These insights resulted in the following associated design opportunities: (1) Maximize personal connection while controlling access, (2) allow case managers to personalize automated client interactions, (3) hold clients accountable to progress and reward achievements, and (4) build a connected, yet confidential community. CONCLUSIONS: Human-centered design enabled us to identify unique insights and design opportunities that may inform the design of a novel and tailored mobile ICT tool for the WAP community.
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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 | 0.032 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".