Specialist tissue viability services: a priority or a luxury?
Bibliographic record
Abstract
During the 1980s, the number of tissue viability nurses (TVNs) rose steadily in the UK, in response to mismanagement of patients with wounds (Fletcher, 1995). Since this time, and in response to the quality agenda, the necessity of promoting a tissue viability service (TVS) that is able to meet the needs of a changing population, while being cost effective and offering interventions based on research and evidence, has grown. The drive to reduce avoidable harm in healthcare and to make efficiency savings is continuing, with TVS being one of the key areas to deliver these targets. However, across the UK we have a wide range of role descriptions and job titles, yet little clarification as to the qualifications and skills required to deliver a successful TVS. Infection control specialist nurses have a clear identity with concise role descriptions representing a range of pay bands. Arguably, this is because they are aligned with a medical specialty, whereas TV is not. The introduction of ‘Any Qualified Provider’ (Department of Health, 2011) has witnessed some services, including management of leg ulceration, being delivered by non-NHS providers at a reduced cost. So is TVS in danger of becoming more of a ‘nice thing’ rather than a priority?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.542 | 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 teacher head, 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".