Bibliographic record
Abstract
Following the recent editorial on social media and its potential impact for health care practitioners and patients alike, two exciting social media-based approaches have been introduced in the wound-care arena. These are The Welsh Wound Community (www.welshwoundcommunity.com) and the Diabetic Foot Canada Community www.diabeticfootcommunity.ca). As the first truly interactive private communities focused on wounds and their prevention and treatment, the sites provide a significant resource to all involved in the care of patients with wounds. Using proprietary software (www.electriceffect.com) the communities allow members to not only communicate privately but also to exchange relevant documents and to share information. The members can then use the community to discuss, disseminate and rank the information posted within the site. This interaction will provide a relevancy score for all content allowing the users to determine which content is highly applicable, which is rated by the community at large and more importantly where evidence gaps exist (i.e. low relevancy areas). Included in the site is the ability to seamlessly viral market both content and news items from the community with other social media channels, such as Facebook and Twitter. One of the common issues of peer-to-peer education and a multidisciplinary approach is continued contact and mentoring. The online community approach provides elements of mentoring and continued contact of the trained educators. The formal virtual private network (social) platform enhances connectivity and provides a vehicle to allow ongoing mentoring, peer-to-peer contact and the feeling of being part of a multidisciplinary approach, even if only virtually. The private network will also allow access to the many assets required to provide effective, evidence-based management and prevention in the area of diabetic foot ulcers and chronic wound management. Through specialised asset management software, the network can demonstrate and record the usage of these assets by region, allowing accurate asset utilisation data—region by region allowing targeted training where it is required. The communities as they more fully evolve will provide private secure networks that facilitate the interaction of both health care professionals and patients alike. These networks will allow the private discussion of issues faced in the management of this growing health care issue and will allow the dissemination, discussion as well as modification of approach and the evidence behind it. The utilisation of this resource will ensure ‘health equality’ across nations while providing a tailored approach for specific populations if required as the network can be segmented to allow ‘specialisation’.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.020 | 0.036 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".