Keeping Clean: A Qualitative Analysis of Water, Sanitation, and Hygiene Among Residents of Recreational Vehicles in Seattle, WA US
Bibliographic record
Abstract
The growing number of people living in vehicles in the urban United States in recent years makes their water, sanitation, and hygiene (WASH) an increasingly relevant public health issue. The WASH context of people living in recreational vehicles (RVs) presents unique challenges, especially as it relates to wastewater disposal. This study answers the question: How do RV residents typically manage their wastewater and meet their other WASH needs? We examined the experiences of RV residents in Seattle, Washington participating in Seattle Public Utilities' mobile pump-out program, which has offered a free, typically monthly, door-to-door RV wastewater disposal service since 2020. We conducted semi-structured interviews with 31 clients and analyzed the data using qualitative content analysis. We found that RV resident water and hygiene experiences were similar to other urban unhoused populations, whereas their sanitation experiences were quite distinct, most notably with regard to open defecation prevalence and the relative risks of shared versus private sanitation. We proposed four WASH service delivery models that consider the possible implications of our findings for cities looking to engage with RV residents. The models included: mobile RV wastewater collection and water delivery, fixed RV dump stations and water resources, public WASH facilities available for all, and safe parking programs.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".