Importance of patient and public involvement in doctoral research involving people living with dementia
Bibliographic record
Abstract
BACKGROUND: There is increasing recognition of the need to include patients and the public in the research process. There is extensive literature about patient and public involvement (PPI) in research, but fewer articles report on PPI in doctoral research. AIM: To reflect on establishing an advisory group for a doctoral study, exploring the opportunities and challenges associated with including patients with dementia in the research process. DISCUSSION: The authors discuss the practicalities of establishing an advisory group, the challenges of being a novice researcher, long-term commitment to PPI, the overall approach to PPI and ethical considerations. CONCLUSION: Establishing an advisory group for a doctoral study can facilitate mutual learning and enhance the study's quality. IMPLICATIONS FOR PRACTICE: Achieving high-quality PPI in health and social care research can ultimately improve its quality and relevance. An important aspect of the doctoral journey is developing knowledge and skills to facilitate PPI as part of a researcher's apprenticeship.
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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 | 0.270 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.011 | 0.016 |
| 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".