Bibliographic record
Abstract
Interdisciplinary research documents the continued global incidence of violence and abuse. Besides its damaging impact on the health and mental health of individuals and families across cultures, the negative consequences of violence extend to communities and the socioeconomic status of whole nations. Yet, survey data reveal that the pivotal role of health and social service professionals in preventing violence and treating victim/survivors has not been actualised systematically primarily because most health sciences faculty do not feel adequately prepared to teach on this topic. While considerable progress on this urgent issue is evident in the health arena over the past two decades, addressing violence issues in formal curricula (theory and supervised clinical practice) to prepare future health and social service professionals for competent practice often hinges on the interest of a particular faculty member in contrast to faculty-wide adoption of this content as an essential element of the curriculum, resulting for some receiving only a single lecture on the topic. Such essentials include the public health prevention facet focusing on vulnerable population groups, and the knowledge, attitudes and skills required by clinical practitioners across disciplines to deliver crisis intervention and treatment on the front lines with victim/survivors, their families and perpetrators. An international consortium of universities in Africa, Australia, Canada, the UK and the USA, aims to alleviate this curricular deficit, building on the research perspective of education as intervention. Traditional and online teaching methods are adapted to varying socio-cultural contexts.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".