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Record W4379384836 · doi:10.1186/s43058-023-00440-4

Defining re-implementation

2023· article· en· W4379384836 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueImplementation Science Communications · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of Calgary
FundersJohnson and Johnson
KeywordsComputer scienceProcess (computing)Process managementIntervention (counseling)Context (archaeology)CINAHLThematic analysisSystematic reviewInclusion (mineral)Management sciencePsychologyPsychological interventionMedicineMEDLINEQualitative researchEngineeringPolitical scienceNursingProgramming languageSociology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.008
Science and technology studies0.0080.001
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.008

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.

Opus teacher head0.791
GPT teacher head0.787
Teacher spread0.004 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it