Human health effects of traffic-related air pollution (TRAP): a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Traffic-related air pollution (TRAP) is one of the major sources of exposure in urban areas and has been associated with a wide range of adverse human health effects. Much of the Canadian population is regularly exposed to TRAP as a result of daily activities (e.g., commuting) and a significant portion of the population resides in close proximity to major roadways. The objective of this scoping review is to develop an evidence map of the epidemiological literature of the human health effects of exposure to TRAP, to support future reviews and assessments by Health Canada. METHODS: Literature searches will be conducted in Ovid EMBASE and Ovid MEDLINE database. DistillerSR will be used to manage the review process. Two reviewers will independently screen the studies in a two-part process (title and abstract; full text) for eligibility. Epidemiological studies and reviews will be included if they report on the human health effects of exposure to TRAP. Data collection will include study design parameters and human health outcomes evaluated in the study. A descriptive analysis will be used to provide a high-level summary of the number of studies evaluating the different types of health effects and cross-tabulations by study design parameters. DISCUSSION: The scoping review will be used to identify subject areas for more detailed review and evaluation of the human health effects of TRAP by the Air Health Effects Assessment Division of Health Canada.
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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 | 0.091 | 0.084 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.082 | 0.020 |
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".