An evaluation of the knowledge and practices of Metro Vancouver residents regarding mould
Bibliographic record
Abstract
Background: The average Canadian spends approximately 90% of their day indoors, a proportion of which may be in public spaces, thereby making Indoor Air Quality (IAQ) a pertinent topic for the fields of Public and Environmental Health. Mould complaints are one of the top IAQ complaints received by Environmental Health Officers (EHOs) in BC. Mould is ubiquitous in both the outdoor and indoor environment. However, once indoors, mould will grow unhindered on most surfaces as long as moisture is present. Accumulating evidence has established relationships between indoor environments and health. Thanks to the Internet, the amount of readily available information regarding mould today is vast but may not necessarily be valid nor reliable. It is important, therefore, to consider what the public does or does not know and where they are getting their information. This study evaluated the public perception of Metro Vancouver residents in regards to mould as an IAQ issue in order to provide Public and Environmental Health practitioners, including EHOs, with a deeper understanding of how to effectively address queries from the public regarding this topic. Methods: Data for this study was collected through a self-administered online questionnaire and disseminated using social media and the snowball effect. Questions were designed to collect demographic information and evaluate the knowledge and attitudes as well as the behaviour and practices of participants. Descriptive and inferential statistics, specifically the independent samples t-test and the analysis of variance (ANOVA), were used to analyze the results. Results: With an average 14.59 out of 20 points, respondent knowledge scores were, in general, fair. There was no statistically significant difference between respondent knowledge score and their gender, age, level of education, income or housing status. Conclusions: Although respondent knowledge scores were fair, a few gaps in knowledge were identified. Further, most of the sample population did not know specifically where to access reliable information on mould. These insights may be useful for Public and Environmental Health professionals when addressing queries from the public regarding this topic.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".