Through Her Eyes: Photovoice as a Research Method for Women with Mental Health Challenges Living in Supportive Housing
Bibliographic record
Abstract
This research explores the subjective experiences of women with mental health challenges residing in a supportive housing building in Southern Ontario. Drawing on principles of Feminist Participatory Action Research (FPAR), five women were brought together to engage in an arts-based workshop meant to provide the opportunity to reflect on their experiences, express themselves through alternative means, and connect with peers over shared experiences. Over six meetings, participants have been invited to contribute to group discussions by taking and analyzing photos that represent their journey while living in supportive housing. Drawing on a critical feminist and Mad Study scholarship, this project used intersectionality as its theoretical lens. This choice intends to emphasize the interconnected and compounded system of oppression that women experience when their identity intersects across various dimensions such as gender, gender expression, race, mental health status, class, and more. This research is essential because of the pervasiveness of discrimination, disempowerment and oppression of people diagnosed with a ‘serious mental illness’ and how these infiltrate relationships and social systems. However, little empirical data exists to explore the in-depth perspective of these individuals, who lack power and voice in society. In particular, women with a psychiatric diagnosis live at the intersection of multiple oppressive factors. Thus, to counterbalance oppression, it is essential to recognize these women as the experts in their lives. Moreover, women’s active participation in research and their photographs and stories offered a nuanced understanding of issues affecting them. From the photographs (visual data) and the meaning given by participants through their stories (narrative data), issues emerged related to stigmatization, gender-based violence, economic limitations, inadequate support, and various forms of discrimination. Furthermore, participants expressed their insights into desired changes within and beyond the supportive housing program. The relevance of this research is threefold: 1) it gave agency to women living in supportive housing to outline their unique needs and wants; 2) it contributed to the paucity of qualitative research situated at the intersection of gender and ‘mental illness’; 3) through KTE activities, it has the potential to inform housing and helping professionals on ways to improve housing projects, design activities, and foster residents’ engagement for this population group.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".