Opinions of parents and parents-to-be on micro- and nanoplastics: knowledge and willingness to implement change in Canada
Bibliographic record
Abstract
Macroplastics (and their degradation products) have been known to have an impact on the ocean and land environment for over 50 years. With the discovery of microplastics and nanoplastics in the human body, recent attention has focused on their potential health effects. Here, a survey was used to gauge the current state of knowledge about microplastics and nanoplastics and willingness to consider plastics-reduction actions in 300 expecting parents and/or parents/guardians of young children in Canada. In total, 79% of participants reported knowing what microplastics and nanoplastics are and 75% were aware of their significant impact on the environment. In contrast, knowledge of potential sources of human exposure to plastics (e.g., household products, food, drinking water) and knowledge about recent preclinical research findings was low. The majority of participants (98%) were willing to consider making at least one change to their homes or daily habits to reduce plastics exposure and participants who reported knowledge about microplastics and nanoplastics were more likely to consider multiple changes in behaviour. To facilitate environmental action, strategies (e.g., email communication, blog, documentaries, social media posts) are needed to improve public knowledge about the potential human health effects associated with microplastics and nanoplastics.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".