Powerlessness, marginalized identity, and silencing of health concerns: Voiced realities of women living with a mental health diagnosis
Bibliographic record
Abstract
Using a feminist qualitative approach, this study substantiated many earlier research findings that document how women with a mental health diagnosis experience unequal access to comprehensive health care compared to the general population. Accounts of this disparity are documented in the literature, yet the literature has failed to record or attend to the voices of those living with mental health challenges. In this paper, women living with a mental health diagnosis describe their experiences as they interface with the health-care system. The participating women's stories clearly relate the organizational and interpersonal challenges commonly faced when they seek health-care services. The stories include experiences of marginalized identity, powerlessness, and silencing of voiced health concerns. The women tell of encountered gaps in access to health care and incomplete health assessment, screening, and treatment. It becomes clear that personal and societal stigmatization related to the mental health diagnosis plays a significant role in these isolating and unsatisfactory experiences. Lastly, the women offer beginning ideas for change by suggesting starting points to eliminate the institutional and interpersonal obstacles or barriers to their wellness. The concerns raised demand attention, reconsideration, and change by those in the health-care system responsible for policy and 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".