Barriers to care for women with low-grade endometrial cancer and morbid obesity: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: Obesity is a major risk factor for low-grade endometrial cancer. The surgical management of patients with obesity is challenging, and they may face unique barriers to accessing care. We completed a qualitative study to understand the experiences of low-grade endometrial cancer patients with morbid obesity, from symptom onset to diagnosis to surgery. DESIGN: ) referred for primary surgery. Transcribed interviews were coded line-by-line and analysed using an interpretive descriptive approach that drew on labelling theory to understand patients' experiences. Thematic sufficiency was confirmed after 15 interviews. SETTING: Two tertiary care centres in Toronto, Ontario, Canada. PARTICIPANTS: (range: 44-70) were interviewed. RESULTS: Thematic analysis identified that (1) both patients and providers lack knowledge on endometrial cancer and its presenting symptoms and risk factors; (2) patients with morbid obesity are subject to stigma and poor communication in the healthcare system and (3, 4) although clinical, administrative, financial, geographic and facility-related barriers exist, quality care for patients with morbid obesity is an achievable goal. CONCLUSIONS: Improved education on the prevention and identification of endometrial cancer is needed for both patients and providers. Delivery of cancer care to patients with morbid obesity may be improved through provider awareness of the impact of weight stigma and establishing streamlined care pathways at centres equipped to manage surgical complexity.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".