Facilitating implementation of the translational research pipeline in neurological rehabilitation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Knowledge translation is a growing area of specialisation. This review summarises the field perspectives and highlights recent work that has particular relevance to neurological rehabilitation. RECENT FINDINGS: Research in knowledge translation can usefully be organised into three overlapping perspectives, namely a linear transfer of codified knowledge, a social interaction perspective, or a multilevel implementation perspective that incorporates contextual factors. Although systematic reviews remain foundational in supporting knowledge translation, they often lack structured updating and can be problematic to implement in complex cases. Knowledge brokers play an important role in evidence use; these may be managers or administrators of rehabilitation services. Organisational support that sustains and structures knowledge brokering roles has been found lacking. Numerous contextual factors influence knowledge translation, including leadership, fidelity monitoring, and divergent stakeholder perspectives. Integrative frameworks have been developed that consolidate the multiple contingencies. SUMMARY: Knowledge translation is a complex process with an incomplete knowledge base; its uniprofessional focus is particularly limiting for neurological rehabilitation. Developing accessible systematic reviews remains central, as well as supporting knowledge brokers, being aware of stakeholder absorptive capacity in developing translational strategies and using integrative frameworks to guide knowledge translation for complex interventions.
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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 | 0.159 | 0.323 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".