Key performance indicators to inform evaluation of wound care programmes for people with complex wounds: a protocol for systematic review
Bibliographic record
Abstract
OBJECTIVE: The objective of the systematic review is to examine and summarise the available evidence in the literature of the use of key performance indicators (KPIs) to inform evaluation of wound care programmes and services for people with hard-to-heal (complex) wounds. The need for wound care is expected to grow with the continued ageing of the population and the resulting increased development of chronic conditions. This expected increase necessitates improvement of wound care programmes and services and their ability to deliver quality, evidence-based and cost-effective practice. The current literature lacks a systematic assessment of KPIs to inform evaluation of wound care services and programmes across various settings, and how the KPIs are used to improve the quality of wound care and achieve desired outcomes. This protocol sets out how the systemtic review will be undertaken. METHOD: Primary studies will be screened from databases such as MEDLINE, CINAHL and Scopus, with unpublished studies and grey literature retrieved from Google Scholar and ProQuest Dissertations and Theses. The study titles and abstracts will be screened by two independent reviewers, using Covidence systematic review software to ensure they meet the inclusion criteria, who will then proceed with data extraction of the full-text using the standardised data extraction instrument. The reference lists of all studies selected for critical appraisal will be screened for additional publications. The two independent reviewers will critically appraise all studies undergoing full-text data extraction using the appropriate checklist from JBI SUMARI. At all stages, differences between reviewers will be resolved through discussion, with adjudication by a third, independent reviewer. RESULTS: Data points will be analysed with descriptive statistics and grouped, based on programme characteristics and publication status. Grey literature and peer-reviewed publications will form separate analyses. To answer review questions, the data will be summarised in a narrative format. A meta-analysis is not planned. At the time of writing, this protocol has been implemented up to the preliminary literature search. CONCLUSION: This review will address a current literature gap and systematically identify KPIs in wound care, allowing for programmes to evaluate their quality of care and improve their services in a methodical manner.
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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 | 0.211 | 0.241 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".