Identifying the gaps in Irish cancer care: Patient, public and providers’ perspectives
Bibliographic record
Abstract
BACKGROUND: The University of Limerick Cancer network (ULCaN) was established in 2019 with funding from the Health Research Institute at the University of Limerick in order to build a network between individuals in academia, primary and secondary care and the general public so that cancer services can be coordinated and more effective. The aim of this paper is to outline our experience of engaging with stakeholders to identify gaps in the cancer journey locally. METHODS: Four focus group discussions were conducted with patients; their carers; members of the public; and healthcare providers with 2 main aims: 1) to investigate gaps in cancer services; 2) to identify knowledge, attitudes and opportunities available to promote cancer research. The focus groups were audio recorded, transcribed and thematically analysed. RESULTS: 15 themes within the topics of cancer care, palliation, communication, clinical trials, diet and exercise and public and patient involvement in research and advocacy were identified. These include directing people to reliable information and navigating misinformation and stigma linked with cancer, promoting awareness of clinical trials and palliative care services and improving communication when multiple healthcare providers are involved. CONCLUSION: The need to make more coherent, efficient and integrated cancer research amongst local stakeholders was evident. Embedding patients and members of the public into ULCaN is an important deliverable for collaborative research.
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".