Domestic Violence Education
Bibliographic record
Abstract
Domestic violence is one of today's most widespread public health problems, yet primary care providers in the United States1 and Canada2 are consistently identified as being inadequately prepared to routinely confront the issue with their patients. At the University of British Columbia in Vancouver, BC, Canada, second-year medical students in the class of 2001 received nine hours of training in patient screening for domestic violence and management of disclosures. The unit involved lectures, video presentations, patient and expert panels, small-group discussions, and role playing. We sought to evaluate whether these students were able to translate the knowledge they had acquired in the classroom into usable skills in an early clinical training experience. We asked 112 students to volunteer to record their experiences doing domestic violence assessments for adult patients during at least six days in the setting of their mandatory rural family practice elective. Thirty-two students agreed to participate, but only six students submitted completed data forms. This was a surprising and disappointing outcome, especially given the high attendance rate for the domestic violence course and the course coordinators' impression that the education sessions had been extremely well received by the students. A few of the students who withdrew from or refused to participate in the study reported that their preceptors had been opposed to their participating (even though it merely involved the students' recording their own clinical experiences), while others cited lack of time or personal reasons for not participating. Overall, the six students who submitted reports described a very low rate of disclosure in the office setting. Despite these student—participants' initial interest in screening for domestic violence, only one of the six regularly conducted screenings of patients. Of all the patients this one student screened, 38% reported histories of domestic violence. The most common reason the students cited for not screening was that they felt domestic violence was “unrelated to the patient's chief complaint/reason for visit.” Additional barriers to screening identified by the students were that the students “did not feel that it was their position or role to discuss this issue with the patient” as well as a concern about the amount of time required to conduct screening. One student commented on the impact of the education received in the classroom versus the reality of clinical practice: “Following the [educational] sessions, I had thought that I would screen for domestic violence; perhaps I was being unrealistic about the true time pressures and discomfort [that I would experience].” One of the most notable observations was that although all six students reported that their preceptors understood the importance of dealing with domestic violence in the family practice setting, none of the preceptors themselves routinely screened for domestic violence. Preceptor modeling of routine screening practices might have been an effective means of converting class-room-learned practices into comfortable clinical habits. Screening for domestic violence is now a standard of practice.3 We must ensure that domestic violence screening is supported in the clinical teaching setting. Classroom-based education may not be enough to enable students to overcome their discomfort with this topic and with their ability to do domestic violence screening as part of routine practice.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.197 | 0.042 |
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".