Our Voices, Our Stories—A Multi‐Method Knowledge Translation Strategy for Advancing Inclusivity and Creating Trauma‐Informed Wound Care
Bibliographic record
Abstract
This initiative utilised knowledge translation (KT) strategies, including digital storytelling (DST) as both a narrative and educational tool, to amplify voices and support trauma-informed healing for individuals living with chronic wounds. A multi-method KT approach was employed, involving: (1) patient DST; (2) a national Patient Journey conference; (3) webinars and conference sessions; (4) a social media campaign; (5) infographics and supplements and (6) an open-access digital library. Since its launch in November 2021, the initiative has garnered significant engagement. Twenty-five patients and care partners across Canada shared their wound care journeys. In June 2022, 191 patients, advocates, policymakers and healthcare providers attended the inaugural virtual Patient Journey. Additionally, 102 participants joined three Patient Journey events between June and October 2024. Patient stories received 23 012 views, and the social media campaign and infographics reached over 900 healthcare professionals, policymakers and advocates across Canada. The initiative raised awareness of the challenges faced by individuals living with wounds. Storytellers described grief, frustration and confusion, underscoring the need for person-centred wound care, timely specialised services and better healthcare navigation. Their experiences revealed care gaps, highlighting the urgent need for systemic change to promote equity and inclusivity in wound care.
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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 | 0.130 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".