FIRE (facilitating implementation of research evidence): a study protocol
Bibliographic record
Abstract
BACKGROUND: Research evidence underpins best practice, but is not always used in healthcare. The Promoting Action on Research Implementation in Health Services (PARIHS) framework suggests that the nature of evidence, the context in which it is used, and whether those trying to use evidence are helped (or facilitated) affect the use of evidence. Urinary incontinence has a major effect on quality of life of older people, has a high prevalence, and is a key priority within European health and social care policy. Improving continence care has the potential to improve the quality of life for older people and reduce the costs associated with providing incontinence aids. OBJECTIVES: This study aims to advance understanding about the contribution facilitation can make to implementing research findings into practice via: extending current knowledge of facilitation as a process for translating research evidence into practice; evaluating the feasibility, effectiveness, and cost-effectiveness of two different models of facilitation in promoting the uptake of research evidence on continence management; assessing the impact of contextual factors on the processes and outcomes of implementation; and implementing a pro-active knowledge transfer and dissemination strategy to diffuse study findings to a wide policy and practice community. SETTING AND SAMPLE: Four European countries, each with six long-term nursing care sites (total 24 sites) for people aged 60 years and over with documented urinary incontinence METHODS AND DESIGN: Pragmatic randomised controlled trial with three arms (standard dissemination and two different programmes of facilitation), with embedded process and economic evaluation. The primary outcome is compliance with the continence recommendations. Secondary outcomes include proportion of residents with incontinence, incidence of incontinence-related dermatitis, urinary tract infections, and quality of life. Outcomes are assessed at baseline, then at 6, 12, 18, and 24 months after the start of the facilitation interventions. Detailed contextual and process data are collected throughout, using interviews with staff, residents and next of kin, observations, assessment of context using the Alberta Context Tool, and documentary evidence. A realistic evaluation framework is used to develop explanatory theory about what works for whom in what circumstances. TRIAL REGISTRATION: Current Controlled Trials ISRCTN11598502.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.207 | 0.153 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.077 | 0.022 |
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".