ASSESSING INTER-RATER RELIABILITY OF ENVIRONMENTAL AUDIT DATA IN A CASE-CONTROL STUDY ON BICYCLING INJURIES
Bibliographic record
Abstract
Background Environmental audit tools must be valid in order to accurately estimate the association between built environmental characteristics and bicycling injury risk. Objectives To examine the inter-rater reliability of a built environment audit tool in a case-control study on the environmental determinants of bicycling injuries. Methods Auditor pairs visited locations where bicycling injuries were known to have occurred in two cities, and recorded location characteristics using the validated Systematic Pedestrian and Cyclist Environmental Scan (SPACES). Case locations were those where a bicyclist was struck by a motor-vehicle (MV), or suffered injuries requiring hospitalisation. Control locations were those where non-MV or minor injuries occurred. Inter-rater reliability of each item on the tool was assessed using observed agreement and Kappa (κ). Results Ninety-seven locations were audited from May–October 2010. Inter-observer agreement was generally high (≥95%); most items had a 1–2% difference in responses. Items with differences ≥5% between raters included path condition, slope, and obstructions. For land use, path, and roadway characteristics, κ ranged from 0.3 for presence of offices and cleanliness to 0.9 for schools and number of lanes; overall, 78% of items had high agreement (κ≥0.61). For MV cases the proportion of items with high agreement was 60%, compared with 73% for controls. For both severe cases and controls, 76% of items had high agreement. Significance Despite low reliability for land use types and cleanliness, percent agreement was high for most items. Our findings suggest that the SPACES tool provides reliable quantitative descriptions of built environmental characteristics at bicycle injury locations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".