Knowledge translation in developing countries
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.
Bibliographic record
Abstract
There is increasing evidence that the application of knowledge in developing countries is failing. One reason is the woeful shortage of health workers, but as this is redressed, it is also crucial that we have an evidence base of what works to minimize the "know-do gap." The World Health Organization and other international organizations are actively building momentum to promote research to determine effective strategies for knowledge translation (KT). At this time, the evidence base for the effectiveness of those strategies is not definitive in developed countries and is relatively sparse in developing countries. It appears, however, that the effectiveness of these strategies is highly variable and dependent on the setting, and success hinges on whether the strategies have been tailored. A useful framework to provide direction for tailoring interventions is the Ottawa Model of Research Use (OMRU). Underlying OMRU is the principle that success rests with tailoring KT strategies to the salient barriers and supports found within the setting. The model recommends that barriers and supports found in the practice environment or as characteristics of potential adopters and the evidence-based innovation or research evidence be assessed and then the KT strategy tailored and executed. The model also recommends that whether the research has been applied and has resulted in improved health outcomes should be measured. Studies in developing countries, although few, illustrate that the OMRU approach may be a valid method of tackling the challenges of KT strategies to improve health care in developing countries.
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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 | 0.007 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it