A Qualitative Needs Analysis of Skin Cancer Care from the Perspectives of Patients, Physicians, and Health Insurance Representatives—A Case Study from Eastern Saxony, Germany
Bibliographic record
Abstract
Skin cancer is one of the most common cancers worldwide and the number of patients is steadily increasing. In skin cancer care, greater interdisciplinary cooperation is required for prevention, early detection, and new complex systemic therapies. However, the implementation of innovative medical care is a major challenge, especially for rural regions with an older than average, multimorbid population, with limited mobility, that are long distances from medical facilities. Solutions are necessary to ensure comprehensive oncological care in rural regions. The aim of this study was to identify indicators to establish a regional care network for integrated skin cancer care. To capture the perspectives of different stakeholder groups, we conducted two focus groups with twenty skin cancer patients and their relatives, a workshop with eight physicians, and three semi-structured interviews with health insurance company representatives. Qualitative data were recorded, transcribed, and analyzed following Mayring's content analysis methods. We generated ten categories based on the reported optimization potentials; five categories were assigned to all three stakeholder groups: Prevention and early diagnosis, accessibility of physicians/clinics, physicians' resources, care provider's responsibilities, and information exchange. The results indicate the need for stronger integration of care in the region. They provide the basis for regional networking as, for example, the conception of treatment pathways or telemedicine with the aim to improve a comprehensive skin cancer care. Our study should raise awareness and postulate as a demand that all patients receive guideline-based therapy, regardless of where they live.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".