Masking in reports of “most serious” events: bias in estimators of sports injury incidence in Canadian children
Bibliographic record
Abstract
INTRODUCTION: Surveys that collect information on injuries often focus on the single "most serious" event to help limit recall error and reduce survey length. However, this can mask less serious injuries and result in biased incidence estimates for specific injury subcategories. METHODS: Data from the 2002 Health Behaviour in School-aged Children (HBSC) survey and from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) were used to compare estimates of sports injury incidence in Canadian children. RESULTS: HBSC data indicate that 6.7% of children report sustaining a sports injury that required an emergency department (ED) visit. However, details were only collected on a child's "most serious" injury, so children who had multiple injuries requiring an ED visit may have had sports injuries that went unreported. The rate of 6.7% can be seen to be an underestimate by as much as 4.3%. Corresponding CHIRPP surveillance data indicate an incidence of 9.9%. Potential masking bias is also highlighted in our analysis of injuries attended by other health care providers. CONCLUSION: The "one most serious injury" line of questioning induces potentially substantial masking bias in the estimation of sports injury incidence, which limits researchers' ability to quantify the burden of sports injury. Longer survey recall periods naturally lead to greater masking. The design of future surveys should take these issues into account. In order to accurately inform policy decisions and the direction of future research, researchers must be aware of these limitations.
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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 | 0.074 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".