Are cost of injury studies useful?
Bibliographic record
Abstract
Studies from the USA and Canada have attempted to estimate the economic costs associated with injury.[1][2][3] The rationale for these studies is often to provide data for priority setting (both research and policy).In other words, the expression of the cost of injury in monetary terms is thought to illustrate the importance of the problem and, therefore, its high priority for research and health services resources.For example, some authors have suggested that policy makers identify "high cost" injuries (compared with other injuries) and make these injuries a priority for treatment and prevention programs.1 3 Cost of injury studies may be useful in the "political" sense, for example, by raising public and political awareness of the burden of injury.Our argument, however, is that such studies are not helpful in the context of setting priorities for resource allocation and research activities.Furthermore, concentration on cost of injury studies may divert policy makers from what they need to know in order to maximise societal benefits from resource allocation.In this paper, we briefly describe the cost of injury method, explain why cost of injury studies have limited usefulness, and explain how, in our view, health economics can better contribute to the field of injury prevention.
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 | 0.106 | 0.409 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".