Bibliographic record
Abstract
One popular theme in the injury prevention literature is the perceived need for more and better surveillance. This arises because of the belief that surveillance is a prerequisite for preventive programs. I have serious reservations about this belief and I could even argue that an undue emphasis on surveillance could be harmful. That, admittedly extreme, view applies when surveillance fails to achieve its most critical objective while consuming resources that could be better directed elsewhere. Two papers in this issue identify limitations in one such emergency department (ED)-based system, the Canadian Injury Reporting and Prevention Program (CHIRPP), but neither speaks directly to my main concern.1 2 CHIRPP is modeled on a similar program (VISS) in Victoria, Australia.3 When I helped to initiate CHIRPP 18 years ago, our primary goal was to use the results to raise the profile of injuries among children.4 Because many more injured children are treated in EDs than die or are hospitalized, we naively assumed that once the public and policy makers became aware of the larger numbers, they would be moved to improve prevention. Unfortunately, despite all the fanfare at its birth and the long interval since then, CHIRPP has prompted few preventive actions. I suspect the same is true for most other such systems. Consequently, I question whether there is any evidence that a surveillance system—even one that operates perfectly—actually contributes to prevention. If not, are there alternatives we should consider? Before going further, let’s agree on the vocabulary: surveillance, surveys, and registries are closely related activities, but are not identical. Unfortunately, the terms are often mistakenly used interchangeably. Surveys are either one-off, episodic, or occur at regular intervals. Most surveys are able to collect detailed data, although recall problems may compromise the accuracy of some of the details.5 Registries …
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 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 | 0.025 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.043 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.024 | 0.040 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".