Trans, Genderqueer, Non-Binary and Gender Non-Conforming Peoples Experiences With Mental Health Care in Ontario
Bibliographic record
Abstract
In this project, I use narrative interviewing and digital storytelling methodologies to understand the experiences of transgender and gender diverse people with the mental health care system in Ontario, as well as their experiences in regard to related health and social policies. My objective is to understand whether the stated goal of the mental health policy in force at the time data was collected—to promote well-being for all Ontarians, including members of groups facing social exclusion—was achieved for members of these communities. I interpret interview and digital story data through the lenses of critical disability studies, disability justice, mad studies, social determinants of health research, feminist new materialism and intersectionality to create a critical analysis of the neoliberal trajectory of mental health-related policy in Ontario. \nWhile participants reported some positive and helpful experiences with the mental health care system, many found it to be inaccessible, culturally unsafe, or even harmful. While participants came into contact with mental health care for a wide range of reasons, many discussed difficulties and harms resulting from the requirement to navigate around gatekeepers in the mental health professions to access gender confirming medical treatment. Participants also reported problems regarding other dimensions of the social determinants of mental health, including income, employment, housing and social inclusion and exclusion. I conclude the project with a discussion of participants' perspectives on the changes needed to support the well-being of the trans and gender diverse communities, arguing for the promotion of cultural safety and improved access to health care, as well as for meaningful changes in regard to the social determinants of mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".