Bibliographic record
Abstract
Intimate partner violence (IPV) or domestic violence is a common and serious public health problem around the globe. Victims of IPV frequently present to health care practitioners including orthopaedic surgeons. Substantial research has been conducted on IPV over the past few decades, but very little research has focused on IPV in the field of orthopaedic surgery. Orthopaedic surgeons may be well positioned to help women who are experiencing IPV and position statements from both the American Academy of Orthopaedic Surgeons and the Canadian Orthopaedic Association exist and provide guidance on the topic. This thesis originated from the lack of understanding of IPV in orthopaedic patients as well as the desire to develop a program for orthopaedic surgeons to assist IPV victims presenting to orthopaedic fracture clinics. The overarching purpose of this thesis was to conduct research to understand the opportunities and challenges facing orthopaedic surgeons in assisting IPV victims in their orthopaedic fracture clinics. The specific aims of this thesis were: 1) to investigate orthopaedic surgeons’, surgical trainees’, and medical students’ perceptions about IPV, 2) to determine the prevalence of IPV in orthopaedic fracture clinic patients, 3) to assess the barriers to and facilitators for screening for IPV in orthopaedic settings, and 4) to discuss the development of a screening program for IPV in orthopaedic fracture clinics.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".