Canadian natural resources large-scale projets : social, cultural and economic impacts : synthesis analysis and annotated bibliography of post-project studies
Bibliographic record
Abstract
This review summarizes the descriptive epidemiology of injuries among women in the United States, highlighting major problems as well as needs and opportunities for intervention and research. Injury mortality rates for 1984-88 were calculated from the National Center for Health Statistics mortality data tapes. Additional injury mortality and all injury morbidity information were derived from existing literature. Studies providing gender-specific U.S. injury information during the last ten years were reviewed. Injuries are the leading causes of death for females to age 34 and are responsible for more years of potential life lost than any other cause of death. The lifetime cost of injuries to females is approximately 50 billion dollars annually. Motor vehicle related injuries, falls, and violence are the most significant injury problems for women. Although morbidity is far greater than mortality, access to information about nonfatal injuries is extremely limited. What evidence does exist points to the importance of domestic assault as a major, underrecognized source of preventable injury. Though the greater magnitude of injury among men frequently eclipses the significance of injury as a problem for women, this paper presents evidence that injury is a problem which should feature prominently in the women's health agenda for the nation. There are pressing research needs to understand the changing trends in injuries to females and to identify appropriate intervention strategies. In addition, the study points to the needs for improvement in data systems to document injury morbidity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".