Prevalence of Residential Dampness and Mold Exposure in a University Student Population
Bibliographic record
Abstract
The impact of residential dampness or mold on respiratory health is well established but few studies have focused on university students. This study aims to: (a) describe the prevalence of exposure to residential dampness or mold in university students according to socio-geographic factors and (b) identify associated housing characteristics. A web survey was conducted in 2014 among the 26,676 students registered at the Université de Sherbrooke (QC, Canada). Residential dampness and mold being closely intertwined, they were considered as a single exposure and assessed using a validated questionnaire. Exposure was compared according to socio-geographic and housing characteristics using chi-square tests and logistic regressions. Among the 2097 participants included in the study (response rate: 8.1%), over 80% were tenants. Residential exposure to dampness or mold was frequent (36.0%, 95% CI: 33.9-38.1). Marked differences for this exposure were noted according to home ownership (39.7% vs. 25.5% among tenants and owners respectively; OR = 1.92%, 95% CI: 1.54-2.38). Campus affiliation, household composition and the number of residents per building were associated with exposure to dampness or mold (p < 0.01), while sex and age were not. Exposure was also associated with older buildings, and buildings in need of renovations and lacking proper ventilation (p < 0.001). This study highlights the potential risk of university students suffering from mold-related health effects given their frequent exposure to this agent. Further research is needed to fully evaluate the mold-related health impact in this at risk group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".