Perceived Water Quality and Reported Health among Adults during the Flint, MI Water Crisis
Bibliographic record
Abstract
Background and Purpose: In April 2014, the municipal water source for Flint, Michigan was changed from Lake Huron to the Flint River. Although residents reported concerns about the quality of tap water and resulting health problems, officials insisted that the water was safe. This study examined relationships between self-reported tap water quality during the water crisis and health conditions among Flint residents. Methods: Participants from each residential Census Tract in the City of Flint were recruited via address lists, online social media, and community-based events. The survey included mental and physical health items from the CDC’s Behavioral Risk Factor Surveillance System and an item on tap water quarter quality experiences. Analyses were weighted to be demographically representative. Results: Participants (N = 277) rated their tap water quality (taste, smell, appearance) as Poor (57%), Fair (20%), Good (13%), Very Good (6%), and Excellent (3%). Controlling for age, gender, years of education, whether respondents were African American or Hispanic/Latino/a, and population demographics, lower perceived tap water quality was associated with worse mental and physical health across all indicators. Conclusion: This study demonstrates associations of tap water quality experiences with reported poor physical and mental health among adults in Flint during the Flint Water Crisis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".