The use of theories, frameworks, or models in knowledge translation studies in healthcare settings in China: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) theories, frameworks, and models (TFMs) can help guide and explain KT processes, and facilitate the evaluation of implementation outcomes. They play a critical role in conducting KT research and practice. Currently, little is known about the usage of TFMs in KT in Chinese healthcare settings. The aim of this scoping review is to identify which TFMs had been used for KT in healthcare settings in China, and how these TFMs were used. METHODS: The protocol for this scoping review is in accordance with the Arksey and O'Malley framework and further enhanced by the recommendations suggested by Levac et al. We will search 8 databases (4 Chinese and 4 English) to identify relevant studies. Four reviewers (2 for Chinese, 2 for English) will independently screen studies based on the eligibility criteria. The basic characteristic of studies and the TFMs utilization (i.e., what, why, and how) will be extracted. Methodological quality and reporting quality will be assessed using the Mixed Method Appraisal Tool (MMAT) and the Standards for Reporting Implementation Studies (StaRI) (or Standards for Quality Improvement Reporting Excellence, SQUIRE 2.0 for quality improvement studies), respectively. All the retrieved TFMs will be categorized into Nilsen's classifications of TFMs for KT research. We will employ the qualitative content analysis approach to summarize how these TFMs have been used, and the rationale. A consultation will be conducted through a 1-h interactive virtual meeting with an expert panel of knowledge users. DISCUSSION: By conducting this scoping review, we expect to gain a comprehensive and in-depth understanding of why and how TFMs have been used in KT research and practice in China, and to identify gaps and provide recommendations for more efficient and meaningful use of TFMs in the future. SYSTEMATIC REVIEW REGISTRATION: This review has been registered with the Open Science Framework (10.17605/OSF.IO/8NXAM).
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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 | 0.203 | 0.167 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".