Experiences and Perceptions of Gynaecological Violence: A Descriptive Exploration of the Phenomenon from Survivors’ Standpoints
Bibliographic record
Abstract
In the context of increasing movements against gender-based violence, resentment against sexism and disrespect within the medical field has gained attention. Using an exploratory qualitative approach, this study aims to provide a comprehensive description of gynaecological violence (GV) from survivors’ perspectives. The convergence of critical thinking, queer theory, and intersectional feminism underscores the importance of understanding diverse and marginalised perspectives as well as individual narratives. Nine participants from Québec (Canada) shared their stories through individual, semi-structured interviews. Phenomenology was used to analyse the deep and nuanced lived experiences, enabling the capture of the essence of GV. The findings first highlight the negative experiences faced by participants, encompassing situations where they felt deprived of choices, unheard, treated insensitively, dehumanised, abandoned, and gaslighted. The results then trace the line between participants’ negative experiences and GV, revealing how perceptions of violence are formed. Finally, a phenomenological description of GV is provided, offering insights into how the phenomenon can be personally experienced by individuals. This study bridges a gap in the literature, provides a foundation for future research and advocacy, and presents recommendations. It calls for reevaluating practices in gynaecological health services to ensure they are inclusive and respectful.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".