Evaluating the outcome and impact of an integrated knowledge translation approach in the development of an equity reporting guideline: a cross-sectional survey
Bibliographic record
Abstract
Background Integrated knowledge translation (IKT) involves active engagement of knowledge users in co-producing research, ensuring their perspectives shape study design, analysis, and reporting. This can strengthen justice, equity, diversity, and inclusion (JEDI) considerations. We adopted an IKT approach in developing STROBE-Equity, an equity-focused extension of the STrengthening the Reporting of OBservational studies in Epidemiology guideline. The perceived value of embedding JEDI principles in reporting guideline development is unknown. This study evaluates the outcomes and impact of such an approach. Methods We conducted a cross-sectional survey of STROBE-Equity project members (n=68) between July–August 2024. The 19-item survey assessed disciplinary background, participation, and perceived benefits, challenges, and impacts of the JEDI-enhanced IKT approach. Inductive content analysis was used to identify themes, which were quantified with frequencies and percentages. Results Thirty-one members responded. Most were aged 35–54 (61%), female (55%), based in Canada (35%), and trained in epidemiology (61%). Reported benefits of IKT included integrating diverse perspectives, inclusive representation, and collaborative learning. Challenges involved accessibility and accommodations, consensus-building, and navigating power dynamics between researchers, policymakers, and those with lived experience. Participants perceived that IKT enhanced dissemination and uptake of STROBE-Equity, improved research design perspectives, and strengthened credibility and applicability. Engagement broadened understanding of social conditions and facilitated incorporation of end-user perspectives, increasing guideline acceptance. Conclusions A JEDI-enhanced IKT approach was viewed as beneficial for reporting guideline development, particularly in fostering inclusivity, strengthening credibility, and improving dissemination. Key challenges such as accessibility and balancing power dynamics highlight areas for improvement. Future research should refine participatory methods to further advance equity in research.
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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 | 0.177 | 0.269 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".