Interprofessional education for practitioners working with the survivors of violence: Exploring early and longer-term outcomes on practice
Bibliographic record
Abstract
Traditionally, practitioners working with the survivors of violence have been offered little in the way of formal education to help them understand why violence occurs and how they can collaborate to support survivors in an effective manner. To help address this need, a team led by one of the authors developed an innovative interprofessional course entitled, "Society, Violence and Practice". The course provided collaborative learning opportunities to practitioners working in various health, welfare and human services settings to equip them further to respond to survivors of violence in a cooperative and holistic manner. To generate an in-depth understanding of participants' views of the impact of the course an exploratory study was undertaken. Drawing upon an interpretivist framework, qualitative data in the form of focus group interviews, individual follow-up telephone interviews, emails and course documentation were collected. Data were analysed inductively to generate themes that could explore the nature of participants' perceptions relating to the differing impact of their interprofessional learning. Findings from this work indicated that the course had a number of early effects in relation to enhancing participants' confidence and knowledge of issues linked to working collaboratively with the survivors of violence. The follow-up data suggested that the course generated a number of longer-term gains on the participants' professional and interprofessional practice. The significance of these findings are discussed in relation to the interprofessional education and adult learning literature before conclusions are presented.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".