The medicalization of sexual assault: a combined quantitative and qualitative study
Bibliographic record
Abstract
Sexual assault is a growing public health problem, both in terms of its negative impact on women's health and the mounting evidence of an often inadequate health care response. There has been a lack of consensus and critical debate about the best health care strategies, a paucity of data about the short and long-term health impacts of sexual assault, and little research that documents the concerns of women who have been sexually assaulted. Drawing on the sociological theoretical framework of feminist social constructionism, this dissertation aimed to examine more closely and clarify the health care implications of sexual assault, especially from the perspectives of women victims/survivors. Three main findings are offered. First, using administrative data derived from the universal Canadian health care program and qualitative data from interviews with 20 women in inner city Toronto who had been sexually assaulted and had sought health care, the health consequences of sexual assault were found to be serious, persistent, and ranging across physical, mental, and social domains. Second, the qualitative analysis revealed that health care following sexual assault can be both helpful and hurtful. These women sought and sometimes received validation that affirmed the significance of their trauma experiences, alleviated fears, provided support, and helped them to cope. Many women also perceived their health care as revictimizing them, due to the invasiveness of physical examinations, the judgmental attitudes of some health providers, and their uncertainty about the role and contingencies of forensic evidence. Third, a comparison of the dominant "expert" models of sexual assault care emerging from North American professional associations and the scientific literature ("medicalization-from-above") with patient perspectives of health care experiences following sexual assault ("medicalization-from-below") suggests that current models guide a less than effective medical response to sexual assault because they inadequately acknowledge the diverse spectrum of expectations and strategies of women, and fail to address the broader social-political context of women's vulnerability and needs. Future studies might examine providers' perceptions and behaviour, and include qualitative research with sexually assaulted women who do not seek formal health care.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".