Applying Evidence in Practice: What We Can Learn from Healthcare
Bibliographic record
Abstract
Applying research findings to practice is the foundation of evidence based practice. In healthcare, evidence-based practice depends upon the development, promulgation and application of clinical guidelines. While EBM has been enthusiastically embraced by many, gaps persist, and transmission from research to practice remains slow and uneven. The perception that EBM threatens professional autonomy accounts for some resistance but, even among practitioners who support EBM in concept, uptake of guidelines has encountered numerous barriers. A recent study of guideline implementation by residents in a tertiary care medical center provides insight into the barriers to guideline adoption, and draws parallels between the uptake of EBM in the healthcare sector and the uptake of EBLIP in the library and information field. Through increased understanding of the diffusion of evidence-based practice in one field, LIS practitioners can position themselves to avoid similar impediments.
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 | 0.102 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.026 | 0.047 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".