Attitudes and perceptions regarding knowledge translation and community engagement in medical research: the PERSPECT qualitative study
Bibliographic record
Abstract
BACKGROUND: The medical research community widely endorses the importance of ensuring that research outputs are relevant and accessible to knowledge users, as well as the value of engaging the latter in the conduct of research to achieve these goals. However, it appears these principles are reflected in actual medical research practices to a limited extent. To better understand this dissonance, we conducted a qualitative investigation into the perspectives of key stakeholders on bridging the knowledge-to-action gap and patient and public engagement. METHODS: The Priorities and Expectations of Researchers, Donors, Patients and the Public Regarding the Funding and Conduct of Medical Research (PERSPECT) qualitative study involved in-depth, semi-structured interviews with representatives of four stakeholder groups. Among other topics, participants were asked to discuss issues related to moving medical research knowledge into action (knowledge translation), including patient and public engagement during the research journey as a prerequisite to the success of this process. We analysed collected data employing an interpretative grounded theory approach. Data collection was ended once thematic saturation had been attained. RESULTS: A total of 41 interviews were completed and analysed (with 10 patients, 10 members of the general public, 11 researchers and 10 funders). Many participants expressed a belief in the importance of engaging patients in the research process, as well as ensuring that study findings reach beyond academic communities. However, multiple challenges and barriers were identified to implementing these values in practice, including: researchers having limited knowledge and tools to foster partnerships with community members; research outputs being inaccessible to the wider public; and the public having insufficient capacity - in view of the required time, effort and knowledge - to assimilate findings and contribute to ongoing research. Cumulatively, interviews indicated a continuing disconnect between research and lay communities, where each stakeholder group holds some responsibility for improving the current paradigm. CONCLUSIONS: Existing gaps in communication, knowledge and relevant competencies are fuelling a disconnect between research and lay communities. Successfully moving research knowledge into action requires joint efforts of multiple stakeholder groups with support from external resources to ensure necessary training, expertise and credible dissemination platforms.
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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 | 0.059 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".