Pragmatism and integrated knowledge translation: exploring the compatabilities and tensions
Bibliographic record
Abstract
AIM: This paper presents a discussion of the role of the philosophy of pragmatism in the integrated knowledge translation approach to research. DESIGN: Critical inquiry is used to discuss bringing pragmatic philosophy and the integrated knowledge translation approach to research together to advance nursing knowledge. METHODS: This paper draws from the literature written on the philosophy of pragmatism and from the current literature on knowledge translation. The possibilities, tensions and limitations for underpinning an integrated knowledge translation research approach with pragmatic philosophy are discussed while highlighting the implications this has for creating knowledge aimed at advancing the practice of nursing. RESULTS: The implications for how nursing knowledge is created in using an integrated knowledge translation approach that is underpinned by pragmatic philosophy are important. Creating nursing knowledge that address the complex problems found in nursing practice is needed. In acknowledging the inseparability of knowledge and practice, researchers, practitioners, policy makers and the public can come together to co-create knowledge that is useful for the practice of nursing. It is these implications of underpinning an integrated knowledge translation research approach with pragmatic philosophy that are significant in creating nursing knowledge that advances the practice of nursing.
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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 | 0.179 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.012 | 0.123 |
| Scholarly communication | 0.030 | 0.046 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".