How to Improve Knowledge Translation of Qualitative Research into Clinical Practice
Bibliographic record
Abstract
ABSTRACT How can qualitative research findings become more influential as trustworthy evidence on which to base clinical practice decisions? Despite an increase in its visibility, the clinical implementation of qualitative findings remains negligible; instead, knowledge users continue to base their clinical decision making primarily on quantitative evidence (Goguen, Knight, & Tiberius, 2008; Shuval, Harker, Roudsari, Groce, Mills, Siddiqi, & Shachak, 2011; Sofaer, 2002).. The purpose of this paper is to describe some of the factors affecting the impact of qualitative findings in the clinical practice of both nursing and medical professionals. This topic is timely and significant because while qualitative research approaches are methodologically and philosophically valid, these approaches remain comparatively lacking in discourse around evidence-based practice and ensuing clinical decisions. These authors continue the academic discussion surrounding the struggle to better translate qualitative research into clinical settings. A brief introduction to qualitative research methods sets a background for endorsing its increased use in clinical practice. The unique contribution of this paper is that practical solutions are provided for incorporating qualitative research into clinical decision-making by healthcare professionals. These are offered to encourage both qualitative and quantitative researchers, clinicians in nursing and medicine, and all knowledge users to take up this problem; despite theoretical and ideological differences, we all have the ultimate goal of utilizing high quality evidence to provide the best care to our patients. Keywords : Knowledge translation, qualitative research, nursing.
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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 | 0.652 | 0.808 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".