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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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 | 0.052 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".