Assessing inter-rater agreement of environmental audit data in a matched case-control study on bicycling injuries
Bibliographic record
Abstract
BACKGROUND: Environmental audit tools must be reliable in order to accurately estimate the association between built environmental characteristics and bicycling injury risk. OBJECTIVE: To examine the inter-rater agreement of a built environment audit tool within a case-control study on the environmental determinants of bicycling injuries. METHODS: Auditor pairs visited locations where bicycling injuries occurred and independently recorded location characteristics using the Systematic Pedestrian and Cyclist Environmental Scan (SPACES). Two case groups were defined: (1) where a bicyclist was struck by a motor-vehicle (MV) and (2) where the bicyclist's injuries required hospitalisation. The two corresponding control groups were (1) where non-MV bicycle-related injuries occurred and (2) where minor bicycle-related injuries occurred. Inter-rater reliability of each item on the tool was assessed using observed agreement and κ with 95% CI. RESULTS: Ninety-seven locations were audited. Inter-observer agreement was generally high (≥95%); most items had a 1-2% difference in responses. Items with ≥5% differences 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 at least substantial agreement (κ≥0.61). For bicyclists struck by a MV the proportion of items with substantial agreement was 60%, compared with 73% for non-MV related injuries. For hospitalisations and minor bicycle-related injuries, 76% of items had substantial agreement. CONCLUSIONS: Agreement was substantial for most, but not all SPACES items. The SPACES provides reliable quantitative descriptions of built environmental characteristics at bicycling injury locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".