Bibliographic record
Abstract
Abstract Background The first attempt to implement a new tool or practice does not always lead to the desired outcome. Re-implementation, which we define as the systematic process of reintroducing an intervention in the same environment, often with some degree of modification, offers another chance at implementation with the opportunity to address failures, modify, and ultimately achieve the desired outcomes. This article proposes a definition and taxonomy for re-implementation informed by case examples in the literature. Main body We conducted a scoping review of the literature for cases that describe re-implementation in concept or practice. We used an iterative process to identify our search terms, pilot testing synonyms or phrases related to re-implementation. We searched PubMed and CINAHL, including articles that described implementing an intervention in the same environment where it had already been implemented. We excluded articles that were policy-focused or described incremental changes as part of a rapid learning cycle, efforts to spread, or a stalled implementation. We assessed for commonalities among cases and conducted a thematic analysis on the circumstance in which re-implementation occurred. A total of 15 articles representing 11 distinct cases met our inclusion criteria. We identified three types of circumstances where re-implementation occurs: (1) failed implementation, where the intervention is appropriate, but the implementation process is ineffective, failing to result in the intended changes; (2) flawed intervention, where modifications to the intervention itself are required either because the tool or process is ineffective or requires tailoring to the needs and/or context of the setting where it is used; and (3) unsustained intervention, where the initially successful implementation of an intervention fails to be sustained. These three circumstances often co-exist; however, there are unique considerations and strategies for each type that can be applied to re-implementation. Conclusions Re-implementation occurs in implementation practice but has not been consistently labeled or described in the literature. Defining and describing re-implementation offers a framework for implementation practitioners embarking on a re-implementation effort and a starting point for further research to bridge the gap between practice and science into this unexplored part of implementation.
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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 | 0.104 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.024 | 0.043 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".