Patient vs. Community Engagement: Emerging Issues
Bibliographic record
Abstract
BACKGROUND: The value proposition of including patients at each step of the research process is that patient perspectives and preferences can have a positive impact on both the science and the outcomes of comparative effectiveness research. How to accomplish engagement and the extent to which approaches to community engagement inform strategies for effective patient engagement need to be examined to address conducting and accelerating comparative effectiveness research. OBJECTIVES: To examine how various perspectives and diverse training lead investigators and patients to conflicting positions on how best to advance patient engagement. RESEARCH DESIGN: Qualitative methods were used to collect perspectives and models of engagement from a diverse group of patients, researchers and clinicians. The project culminated with a workshop involving these stakeholders. The workshop used a novel approach, combining World Café and Future Search techniques, to compare and contrast aspects of patient engagement and community engagement. SUBJECTS: Participants included patients, researchers, and clinicians. MEASURES: Group and workshop discussions provided the consensus on topics related to patient and community engagement. RESULTS: Participants developed and refined a framework that compares and contrasts features associated with patient and community engagement. CONCLUSIONS: Although patient and community engagement may share a similar approach to engagement based on trust and mutual benefit, there may be distinctive aspects that require a unique lexicon, strategies, tactics, and activities.
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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 | 0.150 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 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".