Assessing Tissue Viability Leading Change (TVLC) Framework:Perspective of healthcare professionals
Bibliographic record
Abstract
Background: The National Wound Care Strategy Programme states there are a number of unwarranted variations within wound care across the UK leading to elongated healing times, heightened patient suffering, escalation of costs and increased burden to wound care services. There has been discussion and exploration surrounding the lack of equity across the UK in relation to practitioners being able to access to tissue viability skills and knowledge. The Tissue Viability Leading Change Framework (TVLC) is a framework to assist healthcare practitioners, and those aspiring to work in tissue viability/wound care, to understand the skills and knowledge required in this specialist area of care. TVLC includes 13 different capabilities presented on an online platform. To ascertain which of the 13 capabilities was deemed most important to the user, a survey was distributed to a group of clinicians during the Wounds UK annual conference 2022 aiming to gain greater understanding of the needs and focus of clinical practitioners currently working within the field of wound care. Declaration of interest: The creation of TVLC Framework was supported by an unrestricted educational grant from Urgo Medical.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".