Evaluation and comparison of knowledge translation patterns in selected countries with Iran: A comparative study
Bibliographic record
Abstract
BACKGROUND: One of the main issues related to the inefficiency of the health system is the lack of sufficient communication between researchers and health policymakers regarding the exchange of the latest findings and the use of inappropriate evidence to manage cases. The knowledge translation removes this disconnect. MATERIALS AND METHODS: In this comparative study, to obtain appropriate data on the status of knowledge translation, refer to the databases of reputable centers and governments and the knowledge translation models were reviewed in the title of main articles, abstracts, guidelines, and reports of reputable international organizations between 2005 and 2020. The origin of the models was determined, then the countries with the largest number of models were selected and analyzed using Walt and Gilson's "Policy Triangle framework in four dimensions: context, content, process, and actors." RESULTS: All the three countries have politically, socially, and economically made knowledge translation one of their policy priorities. Iran's centralized health system is a major obstacle. The USA and Canada have clear strategies and coherent and practical infrastructures that implement the knowledge translation in the form of operational plans. In contrast, in Iran, it has been enough to establish the knowledge translation centers at the level of universities and knowledge translation websites. In Iran, the Ministry of Health and universities of medical sciences play a direct role, but in Canada, they also use knowledge broker to apply knowledge. CONCLUSION: Iran is building capacity in the field of knowledge translation. That the implementation of interventions with the cooperation of macro policymakers can strengthen it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".