Exploring the role of the Tissue Viability Nurse
Bibliographic record
Abstract
Aim: To explore the role and identify key responsibilities of the Tissue Viability Nurse (TVN) in the UK. Methods: Mixed methodology using questionnaires distributed via SurveyMonkey and semi-structured interviews. Results: 261 respondents completed the online questionnaire and seven participated in semi-structured interviews. Of the 261 respondents to the questionnaire, 63.7% were employed as TVNs. Almost all respondents claimed to have access to a tissue viability service and the mean TVN team size was 4.7. Some 81.9% of respondents stated they had a team vision, with 75.9% stating that their service had set criteria for referrals. Analysis showed a statistical significance (χ2 (1)=16.6; p<0.001) between TVNs’ and non-TVNs’ knowledge of the referral criteria, with the latter being more aware. There was a variety of other titles used for the role, with interviewees affirming this was poorly understood by patients. Discussion: The results of this study identified that there is no national job title for the TVN role. Data identified that patients do not fully understand the title ‘Tissue Viability Nurse’. The TVN role is complex and not just about the management of a wound. However, what is also clear from the analysis of the data is that there are no clear criteria, or educational level, for the role. Data also suggest that review of current service provision, including partnership working with the multidisciplinary team and industry, is required to develop national competencies, guidance and quality assurance measures.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".