Designer's Corner - Research on Violence and Abuse in Canada: Challenges and Opportunities
Bibliographic record
Abstract
Only fairly recently has there been public recognition of how frequently family members _ most often women and children _ are the targets of violence in their own homes. The risk of experiencing violence from a family member far outweighs the risk of experiencing violence from a stranger, and the consequences for people's health go far beyond the immediate injuries sustained. Health-care professionals have responded by identifying family violence as a serious public health problem and by giving primacy to this issue. Yet, despite the prevalence of violence in our society, the significance of this public health problem, and the strong potential for nurses to initiate prevention efforts and become actively involved in efforts to enhance the health and well-being of abused women and children, there is only a small cadre of nurse researchers in Canada conducting research in this area. To further complicate research efforts, nurses who are actively involved in violence research are scattered across this vast country, making dialogue and collaboration with others in the field a challenge. There is a definite need for more nurses to conduct research in this important area of inquiry, and for those already involved in violence research to seek means of facilitating collaboration with each other. Canadian Nurses' Contribution to Research on Violence and Abuse Although there is only a small number of Canadian nurses conducting violence research, their efforts have made a significant contribution to the body of knowledge on abused women and their children. Studies have provided nursing with an emic perspective of the nature of violence
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 | 0.029 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 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".