Boundary‐making in the medico‐legal context: examining doctor–nurse dynamics in post‐sexual assault forensic medical intervention
Bibliographic record
Abstract
A key dimension of the institutional response to sexual assault is the forensic medical examination of a victim's body conducted for purpose of documenting, collecting and testifying to corroborative evidence. Drawing upon in-depth interviews with forensic examiners and forensic nurse practitioners in one region of England, this study addresses a gap in the existing research on medico-legal processes, and critically examines the nature and dynamics of the relationship between doctors and nurses involved in this intervention. Using an analytic framework based on Thomas Gieryn's notion of 'boundary-work', we explore how this historically gendered dyadic relationship is experienced and understood in a context influenced by both medicine and law. We demonstrate very clear boundaries demarcating (i) physicians as experts and nurses as non-experts in the collection and representation of medical evidence, and, (ii) physicians as equated with technical competence and nurses with 'caring' duties. We conclude by positing implications that may stem from these professional relations with respect to sexual assault evidence, professionals and victims.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.013 |
| 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".