Closing the Distance: A Qualitative Study to Identify Equitable Innovations for Rural Thyroid Cancer Treatment
Bibliographic record
Abstract
BackgroundPatients residing in rural and frontier areas experience worse thyroid cancer outcomes than those in urban areas. This novel qualitative study sought the perspectives of rural surgeons to identify practical measures that could mitigate the disparities in thyroid cancer care between rural and urban contexts.MethodsWe contacted general and head and neck surgeons at all of California's Critical Access Hospitals (n = 35), which are remote, rural hospitals, and requested self-referral to our study through the American College of Surgeons. We performed semi-structured qualitative interviews with surgeons at rural hospitals to understand the assets and vulnerabilities of rural hospitals in providing the highest quality care to patients with thyroid cancer. Responses were coded and analyzed using mixed-methods qualitative analysis methodology.ResultsRural surgeons (n = 13) from a geographically diverse sample of states and regions (AK, AR, CA, NE, NC, NM, TX, UT, WY, and Newfoundland) participated. All initially trained in general surgery; 46% had fellowship training (15% in endocrine surgery) and performed a median of 8.5 thyroidectomies annually.Rural surgeons from all training backgrounds felt adequately trained to treat thyroid cancer and reported a strong desire to provide comprehensive thyroid cancer care. Most reported patients' strong preference to be treated near home. Key challenges to local, comprehensive thyroid cancer care included limited or no access to medical endocrinology, lack of continuing education on thyroid cancer management, and professional isolation in decision-making. Interviewed rural surgeons identified connections with university health systems, expert colleagues, and telemedicine consultations as valuable assets in treating thyroid cancer in geographically isolated hospitals.DiscussionThis study identified key challenges and clear avenues for interventions in treating rural thyroid cancer patients. Interviewed rural surgeons specifically suggest improving access to endocrinology specialists, developing educational initiatives on thyroid cancer management, and fostering connections and collaborations with urban colleagues to reduce professional isolation.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".