Innovation and Design of a Web‐Based Pain Education Interprofessional Resource
Bibliographic record
Abstract
INTRODUCTION: The present article describes educational innovation processes and design of a web-based pain interprofessional resource for prelicensure health science students in universities across Canada. Operationalization of educational theory in design coupled with formative evaluation of design are discussed, along with strategies that support collaborative innovation. METHODS: Educational design was driven by content, theory and evaluation. Pain misbeliefs and teaching points along the continuum from acute to persistent pain were identified. Knowledge-building theory, situated learning, reflection and novel designs for cognitive scaffolding were then employed. Design research principles were incorporated to inform iterative and ongoing design. RESULTS: An authentic patient case was constructed, situated in interprofessional complex care to highlight learning objectives related to pre-operative, postoperative and treatment up to one year, for a surgical cancer patient. Pain mechanisms, assessment and management framed content creation. Knowledge building scaffolds were used, which included video simulations, embedded resources, concurrent feedback, practice-based reflective exercises and commentaries. Scaffolds were refined to specifically support knowledge translation. Illustrative commentaries were designed to explicate pain misbeliefs and best practices. Architecture of the resource was mapped; a multimedia, interactive prototype was created. This pain education resource was developed primarily for individual use, with extensions for interprofessional collective discourse. DISCUSSION: Translation of curricular content scripts into representation maps supported the collaborative design process by establishing a common visual language. The web-based prototype will be formatively and summatively evaluated to assess pedagogic design, knowledge-translation scaffolds, pain knowledge gains, relevance, feasibility and fidelity of this educational innovation.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".