Exploring clinicians’ experiences and perceptions of end-user roles in knowledge development: a qualitative study
Bibliographic record
Abstract
BACKGROUND: End-user involvement in developing evidence-based tools for clinical practice may result in increased uptake and improved patient outcomes. Understanding end-user experiences and perceptions about the co-production of knowledge is useful to further the science of integrated knowledge translation (iKT) - a strategy for accelerating the uptake and impact of research. Our study had two main objectives: (1) explore end-user (clinician) experiences of co-producing an evidence-based practice tool; and (2) describe end-user perceptions in knowledge development. METHODS: We used a qualitative study design. We conducted semi-structured interviews with clinicians and used a transcendental phenomenological approach to analyze themes/phenomena. In addition, we explored the interrelated themes between the thematic maps of each objective. RESULTS: Four themes emerged from clinicians' experiences in co-producing the practice tool: ease/convenience of participating, need for support and encouragement, understanding the value of participating, and individual skillsets yield meaningful contributions. Stakeholder roles in knowledge tool development and improving dissemination of evidence and knowledge tools were themes that related to clinician perceptions in knowledge development. The review of interrelated thematic maps depicts an intertwined relationship between stakeholders and dissemination. CONCLUSIONS: End-users provide invaluable insight and perspective into the development of evidence-based clinical tools. Exploring the experiences and perceptions of end-users may support future research endeavours involving iKT, such as the co-production of clinical resources, potentially improving uptake and patient health outcomes.
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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 | 0.045 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 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".