Parental Preconception Exposures to Outdoor Neighbourhood Environments and Adverse Birth Outcomes: A Protocol for a Scoping Review and Evidence Map
Bibliographic record
Abstract
Parental preconception exposures to built and natural outdoor environments could influence pregnancy and birth outcomes either directly, or via a range of health-related behaviours and conditions. However, there is no existing review summarising the evidence linking natural and built characteristics, such as air and noise pollution, walkability, greenness with pregnancy and birth outcomes. Therefore, the planned scoping review aims to collate and map the published literature on parental preconception exposures to built and natural outdoor environments and adverse pregnancy and birth outcomes. We will search electronic databases (MEDLINE, EMBASE, Scopus) to identify studies for inclusion. Studies will be included if they empirically assess the relationship between maternal and paternal preconception exposures to physical natural and built environment features that occur outdoors in the residential neighbourhood and adverse pregnancy and birth outcomes. Two reviewers will independently screen titles and abstracts, and then the full text. Data extraction and assessment of study quality will be performed by one researcher and checked by a second researcher. Results will be summarised in a narrative synthesis, with additional summaries presented as tables and figures. The scoping review will be disseminated via a peer-reviewed publication, at academic conferences, and published on a website.
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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 | 0.103 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.084 | 0.017 |
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".