Assessing knowledge differences between daycare staff and parents
Bibliographic record
Abstract
Background and Purpose: Most Canadians have lead in their blood and it has been shown that even low levels of lead can cause harm. Children are the most susceptible population to the harmful effects of lead due to their increased absorption and earlier stages of brain development. Lead exposure in children has been shown to have negative and irreversible effects, including delayed development and reduced neurological function. As parents and daycare staff have the most interaction with young children, their health knowledge is important for minimizing day-to-day exposures. This research project assessed the level of knowledge of daycare staff and parents of young children on lead sources and health risks. Methods: An in-person, self-administered knowledge survey was given to parents and Early Childhood Educators (ECEs) at daycare centres located in Surrey, BC and Burnaby, BC. The data was then analyzed using SAS statistical software to compare the two groups using a chi square test. Results: Daycare staff and parents showed no significant differences in knowledge levels. The mean score on the knowledge test for daycare workers was 39.98 +14.77% and for parents was 30.73 +16.53%. Both groups had significant gaps in knowledge on lead, its sources, its risks for children, and preventive measures. Conclusion: Daycare staff and parents have an important role in minimizing children’s exposure to lead. Identifying knowledge gaps in these groups can lead to more targeted health promotion projects as well as changes to education and training.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".