Active Patient Engagement: Long Overdue in Rehabilitation Research
Bibliographic record
Abstract
Patients offer a valuable contribution to developing and improving clinical care.Current foundations and strategies, including the Canadian Foundation for Healthcare Improvement and Canada's Strategy for Patient-Oriented Research, are focused on ways to engage patients' in the development of health services.Furthermore, there is a body of literature reporting on engaging patients in decision-making and policies to promote care delivered across acute settings 1 .In comparison, patient engagement in research has received little attention 2 .The concept of actively involving patients in research is more recent.Patients' role in research has evolved from one which is passive, representing a data point, to one which is active and involves contributing to the research process.According to the Canadian Institute of Health Research (CIHR), 'patient engagement' refers to the meaningful collaboration of patients in the conduct of research and is now a requirement of any application for funding 3 .The CIHR recommends integrating the 'patient perspective' into every step of the research process ranging from conceptualization of a research idea and protocol development through to translation of the research findings into clinical practice.Lessons regarding the involvement of patients in research can be gained from other countries.INVOLVE is a government funded program, supporting active patient engagement in health research across the UK 4 .However, the description of how patients have contributed to the research process is usually only briefly described in published research papers, if at all and therefore the full impact of their involvement is seldom fully understood.Studies need to routinely report detailed information about the method of engaging patients in research and the impact of such engagement on outcomes and continuing research enquiry 3 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.420 | 0.442 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.019 | 0.050 |
| Scholarly communication | 0.045 | 0.063 |
| Open science | 0.009 | 0.036 |
| Research integrity | 0.029 | 0.037 |
| Insufficient payload (model declined to judge) | 0.011 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".