Online strategies to facilitate health‐related knowledge transfer: a systematic search and review
Bibliographic record
Abstract
BACKGROUND: Health interventions and practices often lag behind the available research, and the need for timely translation of new health knowledge into practice is becoming increasingly important. OBJECTIVE: The objective of this study was to conduct a systematic search and review of the literature on online knowledge translation techniques that foster the interaction between various stakeholders and assist in the sharing of ideas and knowledge within the health field. METHODS: The search strategy included all published literature in the English language since January 2003 and used the medline, Cumulative Index to Nursing and Allied Health Literature (cinahl), embase and Inspec databases. RESULTS: The results of the review indicate that online strategies are diverse, yet all are applicable in facilitating online health-related knowledge translation. The method of knowledge sharing ranged from use of wikis, discussion forums, blogs, and social media to data/knowledge management tools, virtual communities of practice and conferencing technology - all of which can encourage online health communication and knowledge translation. CONCLUSIONS: Online technologies are a key facilitator of health-related knowledge translation. This review of online strategies to facilitate health-related knowledge translation can inform the development and improvement of future strategies to expedite the translation of research to practice.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".