How Do Gender, Age and Travel Time Impact on the Need for Social Support of Patients to Have Access to Cancer Treatment?
Bibliographic record
Abstract
Purpose: Disparities in cancer treatment for geographical and socioeconomic reasons have been demonstrated in several countries. In Valais, a canton in Switzerland, to travel to one of the oncology wards can be time consuming, cost intensive and make support by relatives or external persons and institutions necessary. Method: We investigated which kind of support cancer patients in Valais need today to make a treatment possible, quantified it and identified subgroups with particular needs. All patients who came in February 2012 for a consultation or an ambulant therapy to one of the four centres of the “Département Valaisan d’Oncologie” or the unique private practice in the region were asked to answer to a questionnaire. Results were summarised and analysed. Results: 84% of the patients need support. 40% of the patients need two or more kinds of support. Kind and quantity of support depend on gender, age and distance. Cancer patients in Valais need support to make their treatments possible. Some subgroups have a complex pattern of support and need specific assistance as younger women or elderly patients. Conclusions: We demonstrate that cancer patients in Valais need social support to handle their treatment days and that their out of the pocket travel expenses increase rapidly with distance. The pattern of support needed varies according to patient characteristics as gender, age and distance to treatment centre.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".