Hunting to Feel Human, the Process of Women’s Help-Seeking for Suicidality After Intimate Partner Violence: A Feminist Grounded Theory and Photovoice Study
Bibliographic record
Abstract
Women reach out to health care providers for a multitude of health problems in the aftermath of intimate partner violence, including suicidality; however, little is known about how they seek help. The purpose of this study was to explore how women seek help for suicidality after intimate partner violence using a feminist grounded theory and photovoice multiple qualitative research design. Interviews were conducted with 32 women from New Brunswick, Canada, and seven from this sample also participated in five photovoice meetings where they critically reflected on self-generated photos of their help-seeking experiences. Data were analyzed using the constant comparative analysis of grounded theory. Hunting to Feel Human involves fighting for a sense of belonging and personal value by perceiving validation from health care providers. Women battled System Entrapment, a feeling of being dehumanized, by Gauging for Validation and Taking the Path of Least Entrapment. Implications for health care providers include prioritizing validating interactions and adopting a relational approach to practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".