Understanding tailoring to support the implementation of evidence-based interventions in healthcare: The CUSTOMISE research programme protocol
Bibliographic record
Abstract
Although there are effective evidence-based interventions (EBIs) to prevent, treat and coordinate care for chronic conditions they may not be adopted widely and when adopted, implementation challenges can limit their impact. Implementation strategies are “methods or techniques used to enhance the adoption, implementation, and sustainment of a clinical program or practice”. There is some evidence to suggest that to be more effective, strategies should be tailored ; that is, selected and designed to address specific determinants which may influence implementation in a given context. Despite the growing popularity of tailoring the concept is ill-defined, and the way in which tailoring is applied can vary across studies or lack detail when reported. There has been less focus on the part of tailoring where stakeholders prioritise determinants and select strategies, and the way in which theory, evidence and stakeholders’ perspectives should be combined to make decisions during the process. Typically, tailoring is evaluated based on the effectiveness of the tailored strategy , we do not have a clear sense of the mechanisms through which tailoring works, or how to measure the “success” of the tailoring process. We lack an understanding of how stakeholders can be involved effectively in tailoring and the influence of different approaches on the outcome of tailoring. Our research programme, CUSTOMISE (Comparing and Understanding Tailoring Methods for Implementation Strategies in healthcare) will address some of these outstanding questions and generate evidence on the feasibility, acceptability, and efficiency of different tailoring approaches, and build capacity in implementation science in Ireland, developing and delivering training and supports for, and network of, researchers and implementation practitioners. The evidence generated across the studies conducted as part of CUSTOMISE will bring greater clarity, consistency, coherence, and transparency to tailoring, a key process in implementation science.
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 | 0.193 | 0.256 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.098 | 0.023 |
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".