Building Better Website Resources: What People Diagnosed with Sarcoma and Their Carers Want to Know
Bibliographic record
Abstract
People diagnosed with sarcoma and their carers often face significant unmet information needs that adversely affect their quality of life. A contributing factor is the limited availability of accessible, high-quality online information. This study aimed to determine the information needs of people affected by sarcoma from the perspectives of people with sarcoma, carers and healthcare professionals, to inform the development of web-based resources. People with sarcoma (n = 18), carers (n = 11), bereaved carers (n = 8) and healthcare professionals (n = 22) participated in interviews or focus groups (total N = 59). Data were analysed thematically. Nine themes were identified: “Accessing Useful Information About Diagnosis and Treatment”; “Learning to live with Sarcoma”; “Gaining Access to Psychosocial Support”; “Connecting with the Sarcoma Community”, “Obtaining Financial Support”; “Carer Self-Care”; “Facilitating Support for Family”; “Understanding Palliative Care”; and “Preparing for Bereavement and Coping After Death”. Findings support the development of a dedicated sarcoma website as a key step towards addressing their unmet needs. People with sarcoma and their carers highlighted that such a resource would not only improve access to reliable sarcoma-specific information, but also create opportunities for connection and shared experiences among individuals and families affected by sarcoma.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".