Gendered Experiences of Ontological Insecurity Among Women Who Use Drugs and Experience Housing Insecurity: A Critical Narrative Analysis
Bibliographic record
Abstract
Introduction: Research has established that experiences of substance use and housing insecurity leads to violence, alienation, and health deterioration for women. However, no literature has assessed how the gendered nature of substance use and housing insecurity influence the ontological insecurity of women. This paper examines the relationship between ontological insecurity, substance use, and housing insecurity for women. We provide considerations for the theorization of ontological (in)security to account for gender. Methods: Feminist-informed interviews were conducted with 20 women who were clients of a safer supply program located in Kitchener-Waterloo, Ontario. Interviews took place in person, were audio-recorded, and transcribed. Interviews focused on women’s experiences of substance use and housing insecurity across their lives. Data analysis was guided by a feminist re-reading of the theory of ontological security. All women completed a socio-demographic questionnaire. Results: Most women were aged between 22 and 43 ( n = 11), with nine being over the age of 44. Fifteen women identified as white, with five identifying as First Nation, Indigenous, or Metis. Ten women resided in supportive housing units, five resided in transitional housing units, social service agency run motels, or in the private rental market, and five resided in tents or encampments. Women shared how gendered experiences of substance use and housing insecurity, which were associated with trauma and violence, contributed to perceptions of ontological insecurity. Dimensions of ontological insecurity which were discussed by women included a disrupted sense of self, instability, and a loss of autonomy. Conclusion: Our results demonstrate how mechanisms of ontological insecurity for women who use drugs and experience housing insecurity are engrained in the gendered structuring of society. These findings suggest that the current theorization of ontological (in)security is insufficient in examining complete mechanisms which promote ontological security or insecurity for women. Future work which explores ontological (in)security must consider the gendered ordering of society.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| 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".