Bibliographic record
Abstract
INTRODUCTION: There is concern that many national non-fatal injury indicators currently in use are misleading. OBJECTIVE: To make the case for the validation of existing unvalidated indicators, as well as the validation of new indicators before they are promulgated. METHOD: The International Collaborative Effort on Injury Statistics (ICE) Criteria were used for investigating the validity of indicators. Examples of indicators that have been found to be valid using these criteria are presented. In contrast, examples of national road safety indicators are also presented, whose validity is questionable. Trends in road safety indicators with and without threats to validity are contrasted. RESULTS: The New Zealand Injury Prevention Strategy (NZIPS) serious injury indicators are presented as indicators with no identifiable threats to validity. National road safety indicators from Canada, New Zealand and the United Kingdom, with identifiable threats to validity, are also presented. When trends for the valid NZIPS motor vehicle traffic crash indicators are compared with the New Zealand national road safety indicators, which have identifiable threats to validity, they show contrasting trends. This raises concerns that the current national indicators are potentially misleading. CONCLUSION: Validation does matter. For any indicator, it is important that it is clearly defined and specified. The specification should make it clear what parameter the indicator aims to reflect. Before use, the indicator should be validated against this target parameter. That parameter, and the indicators aimed to estimate it, should focus attention on important injuries, ie. injuries that are associated with significant mortality, threat-to-life, threat-of-disablement, loss of quality of life, or increased cost.
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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 | 0.443 | 0.679 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".