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Record W3135509394 · doi:10.1186/s43058-021-00222-w

German translation and pre-testing of Consolidated Framework for Implementation Research (CFIR) and Expert Recommendations for Implementing Change (ERIC)

2021· article· en· W3135509394 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 · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsQueen's UniversityUniversity of Saskatchewan
FundersBundesministerium für Bildung und Forschung
KeywordsGermanComputer scienceImplementation researchProcess (computing)Knowledge translationMedical educationKnowledge managementLinguisticsPsychological interventionMedicineProgramming languageNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Implementation frameworks may support local implementation strategies with a sound theoretical foundation. The Consolidated Framework for Implementation Research (CFIR) facilitates identification of contextual barriers and facilitators, and the Expert Recommendations for Implementing Change (ERIC) allows identifying adequate implementation strategies. Both instruments are already used in German-speaking countries; however, no standardised and validated translation is available thus far. The aim of this study was to translate the CFIR and ERIC framework into German, in order to increase the use of these frameworks and the adherence to evidence-based implementation efforts in German-speaking countries. METHODS: The translation of the original versions of the CFIR and ERIC framework was guided by the World Health Organisation's recommendations for the process of translating and adapting both conceptual frameworks. Accordingly, a four-step process was employed: first, forward translation from English into German was conducted by a research team of German native speakers with fluent knowledge of the English language. Second, a bilingual expert panel comprising one researcher with German as his mother tongue and expert command of the English language and one English language expert and university teacher reviewed the translation and discussed inconsistencies with the initial translators. Third, back-translation into English was conducted by an English native speaking researcher. The final version was pre-tested with 12 German researchers and clinicians who were involved in implementation projects using cognitive interviews. RESULTS: The translation and review process revealed some inconsistencies between the original version and the German translations. All issues could be solved by discussion. Central aspects of the items were confirmed in 60 to 70% of the items, and modifications were proposed in 30% of the items. Finally, we revised one CFIR-item heading after pre-testing. The final version was given consent by all involved parties. CONCLUSIONS: Now, two validated and tested implementation frameworks to guide implementation efforts are available in the German language and can be used to increase the application of agreed-on implementation strategies into practice.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.927
GPT teacher head0.794
Teacher spread0.133 · 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