German translation and pre-testing of Consolidated Framework for Implementation Research (CFIR) and Expert Recommendations for Implementing Change (ERIC)
Bibliographic record
Abstract
Abstract 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.
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 | 0.278 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".