Bibliographic record
Abstract
OBJECTIVES: Knowledge translation (KT) is the dynamic process of mobilizing best-practice evidence to guide health care decisions. METHODS: Using a PubMed search, challenges were identified and milestones defined. RESULTS: Substantial challenges exist in integrating discoveries into patient care, including technical limitations related to genomic testing like turnaround time, standardization, reproducibility, and results interpretation. Other challenges include lack of proper training in genetic counseling for health care providers, clarity of scientific evidence, and ethical, legal and social considerations. In addition, most health care systems lack accessibility to genetic testing services. Moving forward, KT should be addressed at three main frontiers. The first is patients centered for proper understanding and decision making; the second is directed toward health care professionals, including clinical decision support and clarity of roles; and the third addresses resources of health care systems. CONCLUSIONS: Implementing KT requires developing strategies to enhance awareness and promote behavioral changes congruent with research evidence, designing a systematic approach by health care providers and stakeholders to achieve patient-centered care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".