Periodic Assessment of Trajectories of Housing, Homelessness, and Health Study (PATHS): Protocol for a Prospective Cohort Study of People Experiencing Homelessness
Bibliographic record
Abstract
BACKGROUND: The past decade has seen a substantial increase in the number of people experiencing unsheltered homelessness. The unsheltered population faces heightened health and social risks, yet research on their experiences remains limited. OBJECTIVE: This paper presents the protocol for the Periodic Assessment of Trajectories of Housing, Homelessness, and Health Study (PATHS), a longitudinal study that leverages mobile phone technology and web-based surveys to track the housing and health trajectories of people experiencing unsheltered homelessness in Los Angeles County. METHODS: Participants were recruited from the Los Angeles County Homeless Count Demographic Survey, an annual representative survey of the county's unsheltered population. Eligibility criteria included being aged ≥18 years, having stayed in an unsheltered location or homeless shelter for at least 1 night in the past month, and residing in Los Angeles County. The study uses a web-based survey platform accessible via mobile phones and provides electronic gift card incentives for participation. Data on housing, health, and social outcomes are collected monthly using trauma-informed, equity-sensitive surveys, designed for diverse literacy levels with a user-friendly interface that includes buffers for sensitive topics. RESULTS: Since the study launched in December 2021, a total of 2058 individuals have been screened and found eligible. In total, 57.43% (n=1182) of participants completed the baseline survey, of whom 75.47% (n=892) completed at least 1 monthly survey. By December 2024, participants had contributed 7585 monthly surveys (average of 8.5, SD 8.36 per respondent and median of 6, IQR 2-11). Compared to the unsheltered population of Los Angeles County, the PATHS sample overrepresents younger adults aged <40 years (641/1182, 54.23% vs 38.64%) and female participants (507/1182, 42.89% vs 27.74%). Furthermore, the PATHS cohort reports a high burden of health risks relative to the housed population, with 47.3% (422/892) reporting symptoms of anxiety (vs 19.1%), 45.1% (402/892) reporting symptoms of depression (vs 16.4%), 35% (312/892) reporting a disability (vs 12.9%), and 69.4% (619/892) experiencing food insecurity (vs 15.7%). CONCLUSIONS: PATHS offers an innovative platform for real-time monitoring of the housing, health, and service needs of people experiencing unsheltered homelessness in Los Angeles County. By leveraging continuous, in-depth data collection via mobile surveys, PATHS provides valuable insights into the evolving challenges faced by this population. Addressing critical gaps in longitudinal research, PATHS has the potential to drive more informed policy decisions and interventions that improve outcomes for this population considered vulnerable. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/74266.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".