Bibliographic record
Abstract
In the past few years, geographies around the world are struggling with the costs of healthcare with our ever-aging population. Are we simply living too long or not paying enough in taxation to cover the healthcare required? Or are the costs of healthcare mismanaged by systems that have not kept up with the changing demands of healthcare? These are all valid questions and governments are struggling with understanding and funding the solutions. As our focus is wound treatment and prevention, it is our responsibility as ‘leaders’ to help provide the solution and aid in the understanding. National wound initiatives are emerging in different parts of the world and are aiding in the provision of such a lead. Australia led the way with a National Collaborative Research Co-operative type approach driving both research and clinical standardisation. This will provide a coordinated approach to the continued development of wound care as a clinical specialty while providing valuable national data to further convince the government. Currently the Wales government in the UK is working with a number of groups in the principality to develop a national approach for Wales. This will require cross-departmental functioning within government ensuring all stakeholders required are involved and have a common understanding of the issues and more importantly solutions. Within England a group based in the North has come together to establish a similar Centre of Excellence concept and develop an appropriate means to address the changing approach appropriate for the changing healthcare environment in England. This group has commissioned research to provide governmental agencies with the necessary data to drive funding. Similarly within Canada, a number of wound care focused associations and Industry have come together to establish the Wound Care Alliance Canada which has begun the task of involving and convincing government to fund a similar approach applicable to the Canadian geography. Our call to all involved is to share such experiences. We began this process with a recent guest editorial that focused on the Australian CRC. If you are aware of or are involved in any similar initiatives please consider providing us with details of your plans that we will then consider for publication. The IWJ can provide a voice to such initiatives providing a reference source to those wishing to develop systems and structures that are appropriate for their geography focussed in this area. Dr Douglas Queen, Editor, IWJ 1 Professor Keith Harding, Editor-in-Chief, IWJ 2
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 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".