Socioeconomic impact of orthopaedic trauma: Studies on measuring outcomes, estimating effects, and identifying recovery priorities in the United States, Canada, and Uganda
Bibliographic record
Abstract
Orthopaedic trauma is common, affecting over one percent of the global population each year. These injuries are unexpected, often work-related, and frequently affect individuals of lower socioeconomic status. The overarching objective of this thesis was to advance the evidence on the socioeconomic impact of orthopaedic trauma. The thesis endeavored to achieve this objective through three specific aims. The aims were: 1) to describe and evaluate the currently available options for measuring socioeconomic outcomes after orthopaedic injury; 2) to estimate the socioeconomic effects of fractures in three countries with unique healthcare and social welfare systems; and 3) to identify the socioeconomic recovery priorities of fracture patients. The findings of this thesis suggest that orthopaedic trauma has a substantial and sustained impact on the socioeconomic well-being of patients. The effects appear to vary by country and are likely correlated with the availability of health and social insurance programs. Common socioeconomic measures are insufficient for evaluating socioeconomic effects, and innovation for quantifying socioeconomic well-being is required. Understanding patient recovery priorities is essential for optimizing current care pathways and policies to improve value-based care. The failure to mitigate the socioeconomic consequences of injury will not only affect the patient’s socioeconomic well-being but may also negatively affect future health.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".