Perineal warm compresses in the second stage of labour have been demonstrated by clinical guidelines as an effective intervention for improving perineal outcomes and mothers’ psychological well-being, yet their adoption in clinical practice remains suboptimal. Therefore, this study aims to bridge the evidence-practice gap through the application of implementation science models and frameworks to facilitate clinical adoption. The investigation will systematically explore the barriers and facilitators to the application of perineal warm compresses in the second stage of labour and subsequently develop a corresponding implementation strategy addressing identified barriers.

This study was guided by the PEDALs model. Using a scoping review and a parallel mixed-methods study to systematically investigate the barriers and facilitators to the application of perineal warm compresses in the second stage of labour. The identified barriers and facilitators were mapped to the domains of the Consolidated Framework for Implementation Research (CFIR). Then we will use a modified nominal group technique to determine seven priority barriers that need to be addressed. These barriers will be input into the CFIR-ERIC implementation strategy matching tool to obtain expert-recommended implementation strategies. Finally, the Delphi method will be employed to select and refine the implementation strategies into a clear and actionable implementation strategy bundle.

This study has been approved by the Ethics Committee of Hebei Medical University, with approval number 2024043. Written informed consent will be obtained from all participants. Study findings will be disseminated through articles in scientific, peer-reviewed journals, and at national and international conferences. This study will begin in August 2025 and be completed in June 2026.

The purpose of the study is to construct a postoperative nausea and vomiting (PONV) risk prediction model for day-case laparoscopic cholecystectomy (LC) using a machine learning combination algorithm and evaluate its performance.

A retrospective cohort study.

The Hospital Information System (HIS) and the Surgical Anaesthesia Information Management System (SAIMS).

Patient data are collected from the day surgery ward of Sichuan Provincial People’s Hospital from February 2023 to April 2024. The research subjects are adult patients (18–75) who underwent day-case LC, excluding patients with unexpected termination of the day surgery plan, such as the patient who was transferred to hepatobiliary surgery due to intraoperative conversion to laparotomy.

The study employed two data filling methods, two data sampling methods, two variable screening methods and six machine learning algorithms to construct 48 predictive models. Area under curve (AUC), accuracy, precision, recall rate and F1 value were used to evaluate the predictive performance of the model. The AUC of the test set is mainly used to evaluate the prediction performance, and the Shapley weighted explanatory value is used to determine the weight of the variable’s prediction contribution. We will collect patient data from this unit in July 2025 to evaluate the model’s performance.

A total of 2709 patients were selected for model construction in the study. 20 input variables were retained for developing the predictive model. The combined model of KNN, BSMOTE, RFEL and GBM shows the best AUC performance (0.9600). The five most important variables in the prediction model were postoperative pain, LESS method, citraturia dosage, gender and sufentanil dosage. An additional 211 patients were collected to validate the model performance with an AUC of 0.79.

The study finds that postoperative pain, LESS method and cisatracurium dosage are closely related to the occurrence of PONV in day-case LC. However, these three variables have rarely been reported in the previous literature and worth further research. The prediction model obtained in this study provides a meaningful reference for the perioperative prevention and treatment of PONV in day surgery.