Mapping new theoretical and methodological terrain for knowledge translation: contributions from critical realism and the arts
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines have been a popular tool for the improvement of health care through the implementation of evidence from systematic research. Yet, it is increasingly clear that knowledge alone is insufficient to change practice. The social, cultural, and material contexts within which practice occurs may invite or reject innovation, complement or inhibit the activities required for success, and sustain or alter adherence to entrenched practices. However, knowledge translation (KT) models are limited in providing insight about how and why contextual contingencies interact, the causal mechanisms linking structural aspects of context and individual agency, and how these mechanisms influence KT. Another limitation of KT models is the neglect of methods to engage potential adopters of the innovation in critical reflection about aspects of context that influence practice, the relevance and meaning of innovation in the context of practice, and the identification of strategies for bringing about meaningful change. DISCUSSION: This paper presents a KT model, the Critical Realism and the Arts Research Utilization Model (CRARUM), that combines critical realism and arts-based methodologies. Critical realism facilitates understanding of clinical settings by providing insight into the interrelationship between its structures and potentials, and individual action. The arts nurture empathy, and can foster reflection on the ways in which contextual factors influence and shape clinical practice, and how they may facilitate or impede change. The combination of critical realism and the arts within the CRARUM model promotes the successful embedding of interventions, and greater impact and sustainability. CONCLUSION: CRARUM has the potential to strengthen the science of implementation research by addressing the complexities of practice settings, and engaging potential adopters to critically reflect on existing and proposed practices and strategies for sustaining change.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| grok | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| opus | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.151 | 0.183 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.009 | 0.121 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.009 | 0.012 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
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".