“They made me feel like I mattered”: a qualitative study of how mobile crisis teams can support people experiencing homelessness
Bibliographic record
Abstract
BACKGROUND: Mobile crisis teams (MCTs) can be important alternatives to emergency medical services or law enforcement for low-acuity 911 calls. MCTs address crises by de-escalating non-violent situations related to mental health or substance use disorders and concurrent social needs, which are common among people experiencing homelessness (PEH). We sought to explore how an MCT in one city served the needs and supported the long- and short-term goals of PEH who had recently received MCT services. METHODS: We conducted 20 semi-structured interviews with service recipients of the Street Crisis Response Team, a new 911-dispatched MCT implemented in San Francisco in November 2020. In the weeks after their encounter, we interviewed respondents about their overall MCT experience and comparisons to similar services, including perceived facilitators and barriers to the respondent's self-defined life goals. We analyzed interview transcripts with thematic analysis to capture salient themes emerging from the text and organized within a social-ecological model. RESULTS: Nearly all respondents preferred the MCT model over traditional first responders, highlighting the team's person-centered approach. Respondents described the MCT model as effectively addressing their most immediate needs (e.g., food), short-term relief from the demands of homelessness, acute mental health or substance use symptoms, and immediate emotional support. However, systemwide resource constraints limited the ability of the team to effectively address longer-term factors that drive crises, such as solutions to inadequate quality and capacity of current housing and healthcare systems and social services navigation. CONCLUSIONS: In this study, respondents perceived this MCT model as a desirable alternative to law enforcement and other first responders while satisfying immediate survival needs. To improve MCT's effectiveness for PEH, these teams could collaborate with follow-up providers capable of linking clients to resources and services that can meet their long-term needs. However, these teams may not be able to meaningfully impact the longstanding and complex issues that precipitate crises among PEH in the absence of structural changes to upstream drivers of homelessness and fragmentation of care systems.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".