Risk perception of personal care products: A scoping review of knowledge translation strategies in environmental health
Bibliographic record
Abstract
Introduction: Personal care products (PCPs), including cosmetics and skincare products, have seen increased usage over the past two decades. Increasing evidence suggests that certain ingredients in PCPs pose health and environmental risks. For instance, parabens, commonly used as preservatives, are associated with increased cancer risks and reproductive toxicity among women. Despite the availability of safer alternatives, many lack the knowledge to identify harmful substances in PCPs and to seek out alternatives. Knowledge translation (KT) tools offer a solution to bridge this gap by simplifying complex information to improve risk perception. This review aimed to identify effective elements of mobile applications as KT tools focused on environmental health, to increase risk perception and promote behavior change. Methods: A comprehensive scoping review was conducted by searching various databases including PubMed, Google Scholar, Ovid Medline, and CINAHL, yielding 1092 articles. An additional 240 sources related to user-app reviews of 8 mobile apps were identified through a manual Google search. All sources underwent title and (if applicable) screening, followed by full-text review for eligibility. Results: The review included a total of 16 relevant articles, 7 websites, and 6 user app reviews. Key findings revealed 11 elements categorized into 4 main themes: toolkit accessibility and affordability, simplicity of presented information, personalization of features, and a clear focus on knowledge sharing. Conclusion: Using the elements identified in this research, future studies should focus on creating and evaluating environmental health toolkits to build capacity for effective knowledge translation that enhances environmental health awareness and health promotion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".