A systematic review of the barriers, enablers and strategies to embedding translational research within the public hospital system focusing on nursing and allied health professions
Bibliographic record
Abstract
AIM: This systematic review aims to investigate, identify, and compare evidence related to the barriers, enablers, and strategies to embedding translational research within a public hospital system focussed on nursing and allied health disciplines. METHODS: A systematic review looking at the international literature on the barriers, enablers and strategies in embedding translational research within a public health system addressing nursing and allied health professions. The study channelled the PRISMA reporting guidelines for systematic reviews and meta-analyses. Databases searched were Medline, Embase, Scopus and Pubmed from January 2011 to December 2021 (inclusive). A quality assessment was conducted of literature using the mixed methods appraisal tool 2011 version. RESULTS: Thirteen papers met the inclusion criteria. The studies included were from Australia, Saudi Arabia, China, Denmark and Canada. Occupational therapy and physiotherapy were the only two allied health disciplines identified in the search process. The review found considerable inter-relationships between the enablers, barriers, and strategies to embedding research translation in a public hospital setting. Three over-arching themes 'leadership, organisational culture and capabilities' were developed to capture the complexity of factors in embedding translational research. Key subthemes identified were education, knowledge, management, time, workplace culture and resources. All thirteen articles identified that a multifactorial approach is required to embed a research culture and translate research findings into clinical practice. CONCLUSIONS: The themes of leadership, organisational culture and capabilities are inherently intertwined and therefore successful strategies require a whole of health approach with organisational leadership driving the strategy, as changing organisational culture takes time and considerable investment. We recommend that public health organisations, senior executives and policy makers consider the findings of this review to provide evidence to initiate organisational changes to support and help create a research environment to drive research translation within the public sector.
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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 | 0.060 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".