Using developmental research to design innovative knowledge translation technology for spinal cord injury in primary care: Actionable Nuggets™ on SkillScribe™
Bibliographic record
Abstract
CONTEXT/OBJECTIVE: Actionable Nuggets™ for spinal cord injury (SCI) are a knowledge translation tool facilitating evidence-based primary care practice, originally developed in 2010 and refined in 2013. Evaluation results from these two phases of development have informed the design of SkillScribe™, an innovative electronic platform intended to offer reflective continuing medical education (CME) programming through mobile devices in order to support the key features of the Actionable Nuggets™ approach. This brief article describes the ongoing development of Actionable Nuggets™ for SCI on SkillScribe™ by: (1) summarizing the work to date on Actionable Nuggets™; (2) describing evaluation results of Actionable Nuggets™; (3) placing SkillScribe™ in the context of adult education. DESIGN: Developmental Research Design. SETTING: Canadian primary care. PARTICIPANTS: Primary care physicians; specialist physicians. INTERVENTIONS: Twenty educational modules on SCI. OUTCOME MEASURES: Pre- and post-test knowledge survey, feedback and use statistics, impact assessment survey, qualitative analysis of evaluation data. RESULTS: In both hard copy and electronic form, physicians report that Actionable Nuggets™ are an acceptable and useful approach to providing CME for low-prevalence, high-impact conditions like SCI. The key elements of this tool are that they: offer evidence-based information in small, focused "nuggets"; position information where physicians most frequently seek it; offer information in a format that permits direct translation into action in primary care; allow time for reflection; attach practice tools; and offer CME credit. CONCLUSION: Actionable Nuggets™ for SCI, delivered using a convenient and portable electronic medium, with time-released content and interactive testing has the potential to improve the primary care of patients with SCI.
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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 | 0.050 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".