Identifying barriers and facilitators of translating research evidence into clinical practice: A systematic review of reviews
Bibliographic record
Abstract
Translating research into clinical practice is a global priority because of its potential impact on health services delivery and outcomes. Despite the ever-increasing depth and breadth of health research, most areas across the globe seem to be slow to translate relevant research evidence into clinical practice. Thus, this review sought to synthesise existing literature to elucidate the barriers and facilitators to the translation of health research into clinical practice. A systematic review of reviews approach was utilised. Review studies were identified across PubMed, Scopus, Embase, CINAHL and Web of Science databases, from their inception to 15 March 2021. Searching was updated on 30 March 2022. All retrieved articles were screened by two authors; reviews meeting the inclusion criteria were retained. Based on the review type, two validated tools were employed to ascertain their quality: A Measurement Tool to Assess Systematic Reviews-2 and International Narrative Systematic assessment. The framework synthesis method was adopted to guide the analysis and narrative synthesis of data from selected articles. Ten reviews met the inclusion criteria. The study revealed that the translation of new evidence was limited predominantly by individual-level issues and less frequently by organisational factors. Inadequate knowledge and skills of individuals to conduct, organise, utilise and appraise research literature were the primary individual-level barriers. Limited access to research evidence and lack of equipment were the key organisational challenges. To circumvent these barriers, it is critical to establish collaborations and partnerships between policy makers and health professionals at all levels and stages of the research process. The study concluded that recognising barriers and facilitators could help set key priorities that aid in translating and integrating research evidence into practice. Effective stakeholder collaboration and co-operation should improve the translation of research findings into clinical practice.
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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 | 0.107 | 0.332 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.033 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".