“Could you give us an idea on what we are all doing here?” the Patient Voice in Cancer Research (PVCR) starting the journey of involvement in Ireland
Bibliographic record
Abstract
BACKGROUND: Involving patients and their carers in research has become more common, as funders demand evidence of involvement. The 'Patient Voice in Cancer Research' (PVCR) is an initiative led by University College Dublin (UCD) in Ireland. It encourages and enables people affected by cancer, and their families to become involved in shaping and informing the future of cancer research across the island of Ireland. Its aim is to identify the questions and needs that matter most to (i) people living with a cancer diagnosis, and (ii) those most likely to improve the relevance of cancer research. The initiative commenced in April 2016. METHODS: This paper presents a reflective case study of our journey thus far. We outline three key stages of the initiative and share what we have learnt. At the core of PVCR, is a focus on building long-term relationships. RESULTS: We have developed over time an inclusive initiative that is built on trust and respect for everyone's contributions. This work is grounded on collegiality, mixed with a good sense of humour and friendship. CONCLUSION: The development of PVCR has taken time and investment. The benefits and impact of undertaking this work have been immensely rewarding and now require significant focus as we enhance cancer research across the island of Ireland.
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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 | 0.038 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.037 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".