Mapping Journeys of Care: The Impact of Arts-Informed Storytelling Approaches in Improving Diagnosis and Care in Women with PCOS
Bibliographic record
Abstract
Polycystic Ovary Syndrome (PCOS) is the most common endocrine syndrome in people assigned female at birth, affecting up to 25% of individuals worldwide. Symptoms fall into three specific clusters: reproductive, metabolic, and mental health impacts. Reproductive symptoms include ovarian cysts, menstrual irregularities, and fertility-related concerns, and high testosterone. Excess androgen results in changes to one's physical appearance, namely hirsutism and alopecia. Metabolic concerns are insulin-resistance, high body mass index, and dyslipidemia. Higher rates of anxiety, depression, body image disturbances, and a lower quality of life are also reported in PCOS patients. PCOS also increases the risk of type 2 diabetes, cardiovascular disease, endometrial cancer, and non-alcoholic fatty liver disease. Early detection is crucial in mitigating these risks. However, PCOS remains misunderstood, misdiagnosed, and neglected as a chronic health condition, and diagnosis and treatment lags are common worldwide. To understand these delays, and amplify patient-centered research goals, we merged life story approaches with artistic expression to capture and understand diverse patients' lived experiences seeking diagnosis, care, and treatment for PCOS in Canada. Participants first sketched out the chapters of their PCOS story, filled in the details with events/scenes in health care settings that were memorable, and then reflected on their experience holistically, along with their goals for care in the future. In this talk, we will share themes constructed from patients' life stories that capture their intersecting and complex journeys with the health care system. This work illuminates the value of multimethod qualitative approaches to foster health equity in women's health.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".