Exploring women’s perspectives of living with mental illness, stigma, and receiving community services
Bibliographic record
Abstract
According to the Canadian Mental Health Association (CMHA) (2015), one in five individuals will experience mental illness personally, which means that all Canadians will be indirectly (or directly) influenced by mental illness at some point in their life. Unfortunately, due to historical trends and negative stereotypes mental illness has become heavily stigmatized (Camp, Finlay, and Lyons, 2002; Chernomas, Clarke, and Chisholm, 2000; Link, Struening, Neese-Todd, Asmussen, and Phelan, 2001; Sands, 2009; Szeto, Luong & Dobson, 2013). Although many studies have assessed the relationship between mental illness and stigma, little research has included a gender lens when exploring these topics. Therefore, the primary research objective of the current study is to explore women’s perspectives of living with mental illness, stigma, and receiving community services. In total, five women from the Kitchener, Waterloo, and Cambridge area participated in the study. Similar to the literature, results found that the women experienced feelings of loneliness and sadness due to their mental illness diagnosis and the stigma they experienced from friends, family, and service staff. Some women talked about being relieved to have a label or diagnosis for their illness, however, the majority of their narratives suggested that living with a mental illness is difficult due to the internal and external stigma they experience. Findings from this study have implications for contributing to the field of social work, improving service delivery within various healthcare facilities, and future research.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".