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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".