Feasibility and acceptability of a preoperative checklist health promotion in elective surgery in the UK: a mixed-methods study protocol
Bibliographic record
Abstract
INTRODUCTION: Multimorbidity or the presence of two or more long-term conditions is now common in people undergoing surgery. However, current care pathways often miss these healthcare encounters to support long-term health promotion. Therefore, there is a need for practical, scalable approaches that can be integrated into routine surgical care, for which limited solutions exist at present. We have co-designed a structured preoperative checklist to help identify and manage long-term conditions in patients listed for elective surgery. This study aims to evaluate the feasibility and acceptability of this preoperative checklist in patients undergoing elective surgery. METHODS AND ANALYSIS: This is a mixed-methods feasibility study in one National Health Service trust in the UK. We will recruit up to 50 adults scheduled for elective surgery and use the checklist during initial surgical clinic appointments. Quantitative data will include recruitment and retention rates, completion of the checklist and baseline clinical characteristics, analysed using descriptive statistics. Qualitative data will be collected through semistructured interviews with up to 16 patients and clinicians. These interviews will be analysed thematically, guided by the Consolidated Framework for Implementation Research. Triangulation of quantitative and qualitative data will allow us to explore fidelity, acceptability, barriers and facilitators to implementation and refine the intervention ahead of a future pilot cluster randomised trial. ETHICS AND DISSEMINATION: This study has received approval from the Yorkshire & The Humber - Sheffield Research Ethics Committee (approval number: 25/YH/0045). All participants will give written informed consent. Results will be published in peer-reviewed journals and shared with participants, the public and policy stakeholders.
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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 | 0.017 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".