Transferencia del conocimiento: el papel de las guías de práctica clínica
Bibliographic record
Abstract
Despite advances in health research in recent decades there are still gaps between knowledge and daily practice. Several terms have been used in literature to refer to the research that aims at reducing such gaps. Knowledge Translation (KT), the term used in this review, is defined as a dynamic and iterative process that includes synthesis, dissemination, exchange, and ethically-sound application of knowledge to improve health, provide more effective health services and products and strengthen the health care system. This article reviews basic aspects of KT, its differences with translational research, the conceptual framework on which the KT is supported, its scope and objectives, the tools used for the translation, the possible barriers and interventions to counteract them. Clinical practice guidelines, which are based on systematic reviews of the literature, are the ideal tools to synthesize, analyze and contextualize the evidence with the aim to create recommendations, which are the first step when we are interested in planning a KT intervention to be implemented in a target audience.
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 | 0.099 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".