Patient engagement in research; benefits, challenges, importance, and implications
Bibliographic record
Abstract
Abstract Objective Patient engagement (PE) and patient-oriented research have begun to reshape the thought process behind conducting research with the aim of maximizing the relevance of findings for patients. This study aimed to examine the perceived benefits, challenges, importance, and implications of patient engagement from the perspectives of sarcoma patient advisors and researchers. Methods This study utilized a mixed model design. Qualitative data was collected through two focus group discussions with sarcoma patients. Quantitative data was collected via a survey containing Likert scale questions completed by the Centre for Evidence-Based Orthopaedics Musculoskeletal Oncology research team at McMaster University. Results Results showed that patients value the opportunity to contribute to research and support future patients. Being a patient advisor also creates a sense of community and fosters support through building connections and communicating with other patients. Members of the research team noted that patient engagement is important for the study of patient relevant topics and provides insight into the improvement of patient care. However, an added challenge is the lack of current guidance surrounding the implementation of patient engagement. Conclusion These findings emphasize the potential value of patient engagement while also highlighting the need for further research into best practices for the implementation of patient engagement efforts. Overall, patient engagement is an essential area in need of further exploration to enhance future research and clinical trials.
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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 | 0.163 | 0.207 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".