Systematic Search and Evaluation of mobile Apps for Wound Care Available in French-Language in Canada
Bibliographic record
Abstract
Background Wounds are a significant national health concern, impacting individuals, healthcare systems, and the environment. Despite efforts by organizations to promote evidence-based practices, gaps persist between theory and nurse practice in wound care. Mobile apps show promises in enhancing wound care delivery, but their rapid evolution, including adaptations into different languages such as French, raises concerns about reliability and regulation. Evaluating these apps is crucial for ensuring patient safety and effective wound management. Purpose To review and assess mobile wound care apps available in French for healthcare providers in Canada. Methods A systematic search was conducted across the literature and the two main Canadian online app stores (App Store and Google Play). The included mobile apps underwent quality evaluation using the user version of the Mobile Application Rating Scale (uMARS). Results The initial search retrieved 1,550 apps, of which 260 were screened and 5 included. Included apps were from France and were available on both stores. These apps varied in features, including wound dressing directory ( n = 3), best practices reminders ( n = 2), photography management and digital wound tracking ( n = 1), and total body surface area calculator ( n = 1). Evaluation using uMARS indicated total averages range from 3.52/5 to 4.10/5. The results offer scant insight into the design and evaluation of the apps included. Conclusions The study highlights the need for development and validation of a French wound care app tailored to Canadian healthcare contexts and best practice recommendations, emphasizing collaboration among nurses and stakeholders in technology enhancement for the benefit of Canadians’ health.
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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 | 0.019 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".