Bibliographic record
Abstract
Aim: The gap between research and clinical practice leads to inconsistent decision‐making and clinical audits are an effective way of improving the implementation of best practice. Our aim is to assess the effectiveness of a model that implements evidence‐based recommendations for patient outcomes and healthcare quality. Design: National quasi‐experimental, multicentre, before and after study. Methods: This study focuses on patients attending primary care and hospital care units and associated socio‐healthcare services. It uses the Joanna Brigg's Institute Getting Research into Practice model, which improves processes by referring to prior baseline clinical audits. The variables are process and outcome criteria for pain, urinary incontinence, and fall prevention, with data collection at baseline and key points over 12 months drawn from clinical histories and records. Project funding was received from the Spanish Strategic Health Action in November 2014. Discussion: The project results will provide knowledge on the effectiveness of the Getting Research into Practice model, to apply evidence‐based recommendations for the detection and management of pain, urinary incontinence, and fall prevention. It will also establish whether using research results, based on clinical audits and situation analysis, is effective for implementing evidence‐based recommendations and improving patients’ health. Impact: This nationwide Spanish project aims to detect and prevent high‐prevalence healthcare problems, namely pain in patients at any age and falls and urinary incontinence in people aged 65 and over. Tailoring clinical practice to evidence‐based recommendations will reduce unjustified clinical variations in providing healthcare services. Clinical Trial ID: NCT03725774.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.013 |
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".