A just outcome, or ‘just’ an outcome? Towards trauma-informed and survivor-focused emergency responses to sexual assault
Bibliographic record
Abstract
Sexual assault is one of the most violating and traumatising forms of abuse encountered by emergency department providers. Because the vast majority of sexual assaults are perpetrated by someone known, and possibly even related to the survivor, feelings of profound betrayal and loss of trust can make emergency clinical evaluation, including forensic evidence collection, particularly challenging.1 As described in the EMJ paper by Muldoon and colleagues, the collection of forensic evidence by specially trained clinicians may increase the effectiveness of legal prosecution, and is often assumed to be a ‘just’ or, in other words, desired outcome.2 The decision to obtain, and then process, forensic evidence, however, must be an individual, personal decision made by each and every survivor, informed by their own priorities, values and needs, and supported non-judgementally by emergency healthcare providers acting in a trauma-informed manner. ‘Just’ outcomes are unique for each patient and thus should be defined by each individual sexual assault survivor. The survivor’s decisions can be informed, but should neither be directed nor influenced by healthcare providers, administrators, law enforcement personnel, researchers or any other entity. Organisational policies …
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".