Engaging with patients in research on knowledge translation/implementation science methods: a self study
Bibliographic record
Abstract
BACKGROUND: In 2017, the British Columbia (Canada) SUPPORT (SUpport for People and Patient-Oriented Research) Unit created six methods clusters to advance methodologies in patient and public oriented research (POR). The knowledge translation (KT)/implementation science methods cluster identified that although there was guidance about how to involve patients and public members in POR research generally, little was known about how best to involve patients and public members on teams specifically exploring POR KT/implementation science methodologies. The purpose of this self-study was to explore what it means to engage patients and the public in studies of POR methods through the reflections of members of five KT/implementation science teams. METHODS: Informed by a collaborative action research approach, this quality improvement self-study focused on reflection within four KT/implementation science research teams in 2020-2021. The self-study included two rounds of individual interviews with 18 members across four teams. Qualitative data were analyzed using a thematic analysis approach followed by a structured discussion of preliminary findings with the research teams. Subsequently, through two small group discussion sessions, the patients/public members from the teams refined the findings. RESULTS: Undertaking research on POR KT/implementation science methodologies typically requires teams to work with the uncertainty of exploratory and processual research approaches, make good matches between patients/public members and the team, work intentionally yet flexibly, and be attuned to the external context and its influences on the team. POR methodological research teams need to consider that patients/public members bring their life experiences and world views to the research project. They become researchers in their own right. Individual and team reflection allows teams to become aware of team needs, acknowledge team members' vulnerabilities, gain greater sensitivity, and enhance communication. CONCLUSIONS: The iterative self-study process provided research team members with opportunities for reflection and new understanding. Working with patients/public team members as co-researchers opens up new ways of understanding important aspects of research methodologies, which may influence future KT/implementation science research approaches.
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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 | 0.086 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| 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".