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?
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.024 | 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".