Bibliographic record
Abstract
Patients receiving healthcare are commonly exposed to harm that is systematic and often severe. Clinical decisions based on inaccurate sources of information can lead to medical errors, high treatment costs, and poor patient outcomes. Evidence-based practice has the potential to overcome these problems by improving clinical decision-making processes. The PARIHS framework was developed to address the inability of traditional unidimensional models to successfully implement evidence-based practice. The PARIHS framework proposes that successful implementation of evidence into practice is a function of evidence, culture, and facilitation. The PARIHS framework can be used to design, implement, and evaluate knowledge translation projects at both acute and chronic care facilities. This chapter discusses the PARIHS framework as well as its advantages for implementing change at a healthcare setting compared to traditional models. The chapter also outlines a feasible knowledge translation project based on the principles of the PARIHS framework while highlighting health informatics and availability of easily accessible high quality patient outcome data as key enablers in designing and successfully implementing such a project at a healthcare setting.
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 | 0.108 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".