TRANSLATION AND CROSS-CULTURAL ADAPTATION OF THE KNOWLEDGE TRANSLATION PLANNING TEMPLATE FOR THE BRAZILIAN CONTEXT
Bibliographic record
Abstract
ABSTRACT Objective: to translate, cross-culturally adapt, and validate the content of the Knowledge Translation Planning Template, a research dissemination planning tool, into Brazilian Portuguese. Method: this is a methodological study, sequentially divided into six stages: initial translation, translation synthesis, back-translation, judges' committee, pre-test, and approval of the adapted version by the instrument author. The judge's committee assessed content validity using the modified Kappa and Content Validity Index. The test was conducted with teachers and students from a Federal University of Santa Catarina graduate program. Results: the process of translating and back-translating the tool showed no discrepancies in terms of meaning. The committee was composed of seven judges who carried out semantic, cultural, and conceptual evaluations and made notes on the translation of the content. At this stage, the content validity showed excellent values for the Content Validity Index and modified Kappa, with 0.99 and 0.816, respectively. The tool was tested with 30 teachers and postgraduate students, where 90% of the respondents considered the tool to be sufficiently comprehensive and that all the items were relevant to the purpose of the instrument. In the last stage, the documents were analyzed together with the author of the original tool and the final version was approved. Conclusion: the Modelo de Planejamento de Tradução do Conhecimento results from a careful translation process, cross-cultural adaptation, and tool content validation. This has resulted in a tool that is applicable and understood by the target audience, which shows consistency in the equivalence of translation and cross-cultural adaptation for Brazil.
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 | 0.038 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".