Is knowledge translation without patient or community engagement flawed?
Bibliographic record
Abstract
Background: The engagement of patients/individuals and/or communities has become increasingly important in all aspects of the research process. Objective: The aim of this manuscript is to begin the discussion about the use and implementation of authentic engagement in the development of presentations and manuscripts which evolve from research that has engaged patients/individuals and/or communities. Methods: Community-Based Participatory Research; Transformative Action Research. Results and Discussion: In Canada, the framework for engaging patients/individuals and/or communities is clearly outlined in Chapter 9 of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans which indicates that when research projects involving First Nations, Inuit and Métis peoples, the peoples in these communities are to have a role in shaping/co-creating the research that affects them. It is increasingly important that presentations and manuscripts that evolve from results/findings which have engaged patients/individuals and/or communities be co-presented/co-published. Presentations are often done without patients/individuals and/or communities and manuscripts published with only academic authors. Frequently, grants submitted and subsequently funded do not consider this aspect of the process in the budget which makes integrated and outcome knowledge translation, dissemination and distribution by and with patients/individuals and/or communities difficult to facilitate. Conclusions: This manuscript was designed to begin the discussion at various levels related to authentic engagement in the development of presentations and manuscripts which evolve from research that has engaged patients/individuals and/or communities. How will you include patients/individuals and/or communities in your presentations and publications?
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 | 0.436 | 0.654 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.039 | 0.043 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".