“I felt like she didn’t take me seriously”: a multi-methods study examining patient satisfaction and experiences with polycystic ovary syndrome (PCOS) in Canada
Bibliographic record
Abstract
BACKGROUND: Polycystic ovary syndrome (PCOS) is a common and complex condition affecting metabolic, reproductive, cardiovascular, and psychological health in women. Previous studies point to widespread dissatisfaction and delays with the diagnosis of PCOS and a lack of information provision by doctors, with few studies on Canadian populations. This multi-methods study explored the perceptions and experiences of PCOS diagnosis in Canada using an online-distributed survey and follow-up, in-depth, semi-structured phone interviews. METHODS: The online questionnaire was completed by 296 women aged 18-60 with a self-reported diagnosis of PCOS. The survey measured time to diagnosis, number of doctors seen, and information provided. Descriptive statistics, Chi-square tests, Fisher's exact tests, and Spearman's rank correlations were used to analyze the sample characteristics and correlations between the demographic factors and the outcome measures. Twenty-five follow-up interviews were held over the phone and analyzed using thematic analysis and interpretive description methodology. RESULTS: Survey respondents were dissatisfied with the information provided about PCOS (66%), lifestyle management (34%), and medical therapy (38%) at the time of diagnosis. Approximately 34% of respondents waited for more than 2 years and 41% saw 3 or more doctors before attaining diagnosis. Many did not receive any information about lifestyle management (42%) or medical therapy (28%). Interview participants encountered doctors who lacked sufficient knowledge on PCOS to diagnose, chronically dismissed concerns, and did not provide necessary medical information about the condition. Women described benefitting from self-advocation to receive the care they needed from doctors, and self-educating about PCOS using materials they could find online. Younger interview participants whose symptoms began in adolescence would often not understand the significance of symptoms until much later in life, contributing to delayed diagnoses. CONCLUSIONS: Greater education on PCOS for physicians, particularly in primary care, is needed to prevent delayed diagnoses and ensure that patients are provided with reliable medical information about their condition. Greater awareness of PCOS may be necessary for the general population to help women identify symptoms, especially for adolescents and their parental figures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".