The Canadian Wound Care Education Framework
Bibliographic record
Abstract
Purpose This article aims to outline a conceptual framework for the development of wound care knowledge and illustrate how members of the interprofessional health care team can enhance their understanding and practice of wound management within the Canadian context. Methods We used a qualitative descriptive research design with a qualitative content analysis approach for this study. A review of the literature regarding professional education frameworks combined with an exhaustive search of Canadian wound care education programs, guided the data collection of this study. To guide the development of the conceptual framework, we utilized a metaphor of a tree and identified educational pathways through both academic and experiential methods. Results The Canadian Wound Care Education Framework is presented along with the Model of Canadian Wound Care Education. This framework will assist those looking to develop their understanding of wound management and the options available to do so within a Canadian context. This framework will also provide an overview of available educational opportunities, gaps in the current wound care educational options and an improved understanding of wound management knowledge creation. Conclusions We have introduced a comprehensive conceptual framework for developing wound care knowledge and guiding learners through multiple educational pathway options in Canada. Utilizing a tree metaphor and exploring academic and experiential branches offer insight into available wound care education opportunities. Furthermore, the framework outlines gaps within the current wound care educational landscape in Canada and supports those looking to improve their understanding of wound management through a deeper understanding of knowledge creation and learning progression within a Canadian context.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".