Evaluation of the effectiveness of arsenic screening promotion in private wells: a quasi-experimental study
Bibliographic record
Abstract
The Eastern Townships (ETR) is a region in Québec (Canada) where the soil is naturally rich in arsenic (As). About a third of the people in the ETR obtain their water from a private well. A quasi-experimental design was used to compare two campaigns designed to promote As screening in well water: a mass-media campaign (MMC) followed or not by a community-based intervention (CBI). The MMC is based on a press release issued for the ETR, along with a leaflet on As made available on the Internet, and in strategic places. The CBI, formulated according to the factors of the Precede-Proceed model, was aimed at mobilizing local authorities and small media. It targets only one municipality; the intervention community (IC). Using a separate pre-post samples design, two population-based cross-sectional (pre-CBI and post-CBI) surveys were conducted by phone at 6-month intervals, by means of random samples. The samples counted, for the IC and the ETR, respectively, 87 and 156 well owners in pre-CBI, and 106 and 190 in post-CBI. The results in post-CBI showed that the proportion of well owners who had their water test increased by four times in the IC after (16% p = 0.004). When adjusting for age and gender among all the post-CBI respondents, As screening is related with intervention status (exposed to MMC and CBI; p ≤ 0.001) and on previous microbiological water analysis behavior (p ≤ 0.05), but is not related to knowledge. This study demonstrates the superiority of a community-based campaign over a MMC when environmental health is concerned.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".