Skip to main content
iScience logoLink to iScience
. 2026 Sep 23;29(10):117490. doi: 10.1016/j.isci.2026.117490

Preventive trial-and-error model is associated with improved breech delivery skills and training transfer in midwifery interns

Ying Zhuge 1, Xintong Yao 2, Xiong Deng 1, Hongxiang Yao 1,3,∗
PMCID: PMC13634362  PMID: 42830952

Summary

Breech delivery requires midwifery trainees to integrate technical skills, clinical decision-making, and error prevention in time-sensitive situations. We evaluated a preventive trial-and-error model using high-fidelity simulation among 89 midwifery interns in a quasi-experimental study. Over 4 weeks, the intervention incorporated structured exposure to simulated errors, guided correction, preventive strategy discussion, and feedback, whereas the control group received conventional demonstration-based training. Compared with controls, the intervention group achieved higher breech delivery skill scores (89.65 ± 3.42 versus 85.53 ± 2.64) and training transfer scores (25.83 ± 1.78 versus 22.88 ± 1.62). These findings suggest that structured error-based simulation may support short-term skill acquisition and perceived transfer of learning in midwifery education. The approach provides a practical framework for integrating error prevention and reflective learning into simulation-based training when opportunities for repeated clinical exposure are limited.

Keywords: breech delivery, midwifery education, high-fidelity simulation, trial-and-error learning theory, preventive trial-and-error model

Highlights

  • •

    Preventive trial-and-error training improved breech delivery skills

  • •

    Error anticipation was integrated into high-fidelity simulation training

  • •

    The model improved interns’ perceived transfer of training to clinical practice

  • •

    Structured error-based learning offers a practical approach to midwifery education


Nursing education; Nursing practice

Introduction

Breech delivery has long been a key research area of authoritative organizations1,2 such as the International Confederation of Midwives and the National Center for Obstetric Care Quality Control due to its significant maternal and neonatal risks and clinical complexity.3 Recent consensus-based recommendations for the care of women with a breech-presenting fetus have emphasized the importance of appropriate training and preparedness for healthcare professionals involved in breech birth.4 These recommendations highlight the need for structured and progressive training approaches in midwifery education.

High-fidelity simulation (HFS) teaching, as an innovative teaching method that integrates computer physiology-driven technology and clinical scene simulation, has been proven to effectively improve students’ general clinical capabilities and professional literacy,5,6,7 but there is still room for optimization in terms of teaching design and training effects in existing courses and systems.8,9,10 This study introduces the “trial and error” learning theory proposed by American psychologist Thorndike, which emphasizes the establishment of correct response connections through error exposure and behavior correction. This theory has been widely applied in simulated medical education and has achieved results.11,12 Based on this, our hospital has integrated the theory of “trial and error” into the case teaching method since 2023 and constructed a four-stage preventive trial and error teaching model (preventive trial-and-error model) of “creating error cases-guiding error correction-discussion, prevention-evaluation feedback.” Through the high-fidelity simulator system, typical clinical error scenarios are preset to guide students to expose errors in a safe environment, correct behaviors, and systematically discuss error prevention mechanisms. This model is initially applied in the breech delivery skills training of midwifery interns in grades 2023–2024, achieving ideal teaching results.

Results

Intern’s mastery of breech delivery skills

The overall structure and progression of the three-session training program are illustrated in Figure 1, providing an overview of the case-based learning activities and their sequential implementation. A database of error cases for breech delivery skills was established, with the main error cases and corresponding teaching scenarios presented in Table 1. This study compared the mastery level of breech delivery skills between two groups of interns. The results showed that the Intervention group interns had significantly higher mastery of breech delivery skills than the control group, and there was a statistical difference between the two groups (p < 0.05), as shown in Table 2. Specifically, the average mastery level of breech delivery skills among the control group interns was 85.53 ± 2.64, while the Intervention group was 89.65 ± 3.42.

Figure 1.

Figure 1

Course content arrangement of the preventive trial-and-error training model

Table 1.

Database of error cases for breech delivery skills

Case number Case name Error description Difficulty
1 Ignoring the warning of umbilical cord prolapse Failing to promptly identify the risk of umbilical cord prolapse after the rupture of fetal membranes ★
2 Incorrect handling of premature rupture of membranes In the Breech presentation, with premature rupture of membranes and the cervix not fully dilated, not immediately taking measures such as elevating the buttocks, having the parturient lie on her side, or reducing the prolapsed limb ★
3 Inappropriate intrapartum management (delayed decision-making for cesarean section) Failing to promptly assess fetal distress and continuing to attempt vaginal delivery for more than 30 min. Delayed communication between the midwife and the obstetrician leads to decision-making delays ★
4 Lack of key information in intrapartum records Not recording the time of fetal head descent arrest and the corresponding treatment measures ★
5 Neglecting the psychological care of the parturient Not providing active intervention for the parturient’s pain, anxiety, fear, and other emotions ★★
6 Misinterpreting fetal heart monitoring Mistaking late decelerations for early decelerations and not initiating emergency rescue measures ★★
7 Incorrect postpartum assessment Not checking for neonatal hip dislocation (a common complication of breech delivery) ★★
8 Incorrect breech traction technique Difficulty in the delivery of the fetal shoulders with the arms raised. Using excessive force or an incorrect traction angle (such as pulling upward instead of rotating downward), resulting in the model’s arm falling off ★★★
9 Ignoring the intensity of uterine contractions during the plugging of the buttocks The midwife only performs buttock-plugging. Team communication fails, and the lack of uterine contractions is not detected in time ★★★
10 Incorrect resuscitation position Not keeping the neonate in a head-down position after delivery, leading to amniotic fluid aspiration ★★★

Difficulty: ★, low difficulty; ★★, moderate difficulty; ★★★, high difficulty.

Table 2.

Comparison of breech delivery skill mastery between the two groups of interns

Group (mean ± standard deviation) Total score
Control group (n = 43) 85.53 ± 2.64
Intervention group (n = 46) 89.65 ± 3.42
T −6.372
P p < 0.001

Data are presented as mean ± SD.

Training transfer effect among interns

Evaluate the interns’ confidence in translating the results of high simulation training into practical applications through the training transfer effectiveness scale (total score of 30 points). The total score of the Intervention group was significantly higher than that of the control group (25.83 ± 1.78 vs. 22.88 ± 1.62, p < 0.05). Among the six sub-items, the Intervention group scored significantly higher than the control group in Q1, “After receiving training, my knowledge reserve and skill level have been improved” (4.50 ± 0.59 vs. 3.63 ± 0.72), Q2, “After the training, I know how to apply the knowledge and skills learned in the training to my work” (4.20 ± 0.54 vs. 3.86 ± 0.56), Q3, “After the training, my work enthusiasm has been improved” (4.33 ± 0.70 vs. 3.70 ± 0.60), Q4, “I am more confident in accepting new work tasks and challenges” (4.28 ± 0.62 vs. 3.88 ± 0.63), and Q6, “My work behavior has been improved and this good behavior has been maintained” (4.48 ± 0.66 vs. 3.91 ± 0.75) (all p < 0.01). Q5, “I applied the knowledge and skills learned in the training, thus improving my work performance,” did not differ significantly between the two groups (p > 0.05), as shown in Table 3.

Table 3.

Comparison of training transfer effects between the two groups of interns

Group (mean ± standard deviation) Q1: After receiving training, my knowledge reserve and skill level have been improved. Q2: After training, I know how to apply the knowledge and skills learned in training to my work. Q3: After training, my work enthusiasm and initiative have increased. Q4: I am more confident to take on new tasks and challenges. Q5: I applied the knowledge and skills learned in training to my work, thus improving my work performance. Q6: My work behavior has been improved and this good behavior has been maintained. Total score of training transfer effects
Control group (n = 43) 3.63 ± 0.72 3.86 ± 0.56 3.70 ± 0.60 3.88 ± 0.63 3.91 ± 0.61 3.91 ± 0.75 22.88 ± 1.62
Intervention group (n = 46) 4.50 ± 0.59 4.20 ± 0.54 4.33 ± 0.70 4.28 ± 0.62 4.04 ± 0.82 4.48 ± 0.66 25.83 ± 1.78
T −6.258 −2.868 −4.532 −3.019 −0.898 −3.826 −8.134
P < 0.001 0.005 < 0.001 0.003 0.372 < 0.001 < 0.001

Data are presented as mean ± SD.

Discussion

Effectiveness of the preventive trial-and-error model

The results of this study showed that the assessment score of the skills in breech presentation delivery in the Intervention group (89.65 ± 3.42 points) was significantly higher than that in the control group (85.53 ± 2.64 points, p < 0.05), which is highly consistent with the conclusion that “error-driven learning can improve clinical skills.”11,13,14 This result verifies the core hypothesis of the preventive trial-and-error model, that is, through systematic error exposure and prospective preventive strategies, it can effectively promote the mastery of skills. Different from the traditional trial-and-error approach that only focuses on “error correction,” in this study, through the preset error case database15 and the four-stage teaching method,16 errors are transformed into manageable learning resources.17

In-depth analysis of the training transfer effect

This study conducted hierarchical and progressive training based on the difficulty level of the case18,19 and found that the total score of the training transfer effect in the Intervention group (25.83 ± 1.78) was significantly higher than that in the control group (22.88 ± 1.62, p < 0.05), indicating that the preventive trial-and-error model can effectively enhance the confidence of interns in transforming simulated training into clinical practice.20 HFS can improve the transfer effect of skill training.6,9 It is worth noting that Q5 “I applied the knowledge and skills learned in the training to my work, thus improving my work performance” did not show a difference between the groups (p > 0.05). The possible reasons for this analysis are as follows. There are several limitations of clinical practice: in China’s medical system, the decision-making power for breech presentation delivery is usually dominated by obstetricians, and midwifery interns mainly play an auxiliary role, resulting in limited opportunities for skill application.21 To discuss the influence of organizational culture, in traditional clinical teaching, the inertia of “executing according to doctor’s orders” may affect the willingness of interns to actively apply new skills.22,23

This study promotes the innovative development of the trial-and-error theory. The results of this study expand the classic trial-and-error theory in the following aspects. Structured design of error exposure: By establishing a database of 10 standardized error cases, it breaks through the inefficiency of traditional random trial-and-error methods, echoing the “active error management” theory.24 Pre-emptive prevention mechanism: In the discussion session, brainstorming and fishbone diagrams are used for root cause analysis, transforming Reason’s “Swiss cheese model”25 into a specific teaching tool, guiding interns to expand their thinking and shifting error prevention from “post-event remedy” to “pre-event design.” Closed-loop nature of the feedback system: Combining the data review of high-fidelity manikins, multiple offline trainings, and skill assessments, a complete learning cycle of “operation, feedback, and reinforcement” is formed.26

Implications of this study for midwifery education practice. Innovation of the teaching model: Existing research has shown that HFS training can improve clinical skills and preparedness for breech delivery. This study combines HFS with trial-and-error theory, the establishment of a database of error cases for breech presentation delivery skills, and stratification according to case difficulty, providing a feasible approach to structured and progressive training in midwifery education. Given the limited nature of HFS teaching resources, a regional error case and curriculum sharing platform27 can be established to address the problem of insufficient teaching resources in primary hospitals. Transformation of the teacher’s role is as follows: teachers transform from “knowledge transmitters” to “error guides.” Teacher training workshops can be carried out to improve the ability to design error cases and provide feedback.28

This study suggests that the preventive trial-and-error teaching model represents a potentially valuable extension of classical learning theories and may contribute to improving training in midwifery safety. However, its findings are based on a single-center cohort of midwifery interns in China and primarily reflect short-term skill acquisition and self-reported training transfer. Therefore, multi-center and cross-cultural validation is necessary before broader application. In the context of limited opportunities for breech delivery practice, its potential value may also lie in fostering error-preventive clinical thinking, which may be an important competency for managing the complexity of modern obstetrics.

Limitations of the study

Although this study demonstrates preliminary positive findings, several limitations should be acknowledged. First, although the sample size met the required statistical power (81.2%), the single-center design may limit external validity, and future multi-center studies are needed to enhance generalizability. Second, the error scenarios were specifically designed for the preventive trial-and-error training model, which may introduce a degree of bias favoring the intervention, similar to an in-distribution evaluation setting. Therefore, the applicability of the findings to unanticipated or novel clinical error situations requires further validation. Third, only short-term learning outcomes were assessed, and long-term skill retention and clinical transfer were not evaluated. Fourth, the current evaluation only includes teacher-rated skill performance and student self-reported training transfer, which may be relatively limited in scope. Future studies are encouraged to adopt a more comprehensive and multi-dimensional evaluation framework to enhance robustness. Fifth, in-class evaluations relied mainly on subjective teacher assessment. Future work could develop an AI-driven intelligent evaluation system to improve objectivity. Finally, all participants were female. Therefore, the influence of sex on the effectiveness of the intervention could not be evaluated, which may limit the generalizability of the findings to male learners.

Resource availability

Lead contact

Further information and requests for resources should be directed to and will be fulfilled by the corresponding author, Hongxiang Yao (lsyhx2004@163.com).

Materials availability

This study did not generate new unique reagents.

Data and code availability

  • •

    Data reported in this study will be shared by the lead contact upon request.

  • •

    This study does not report original code.

  • •

    Any additional information required to reanalyze the data reported in this study are available from the lead contact upon request.

Acknowledgments

This study was supported by the Special Fund for Clinical Scientific Research of Wu Jieping Medical Foundation (no. 3206750, 2020-10-36).

Author contributions

Y.Z.: data curation, investigation, writing – original draft, and formal analysis; X.Y.: data collection and analysis; X.D.: methodology and resources; H.Y.: conceptualization, funding acquisition, supervision, and writing – review and editing.

Declaration of interests

The authors declare that they have no conflicts of interest.

STAR★Methods

Key resources table

REAGENT or RESOURCE SOURCE IDENTIFIER
Software and algorithms

G∗Power 3.1 Heinrich-Heine-Universität Düsseldorf G∗Power 3.1
IBM SPSS Statistics 22.0 IBM Corp. Version 22.0

Other

High-fidelity simulation system Laerdal Medical SimMom
Training Transfer Effect Scale Lin, W., et al. Lin, W., et al.29

Experimental model and study participant details

Study participants

This was a non-randomized, quasi-experimental before-and-after study using two sequential cohorts of midwifery interns. The Intervention group (n = 46), enrolled between July 2023 and February 2025, received breech delivery training using the preventive trial-and-error pedagogical model incorporating error anticipation and structured feedback. The control group (n = 43), recruited between July 2021 and March 2023, received conventional demonstration-based training.

The inclusion criteria were as follows: midwifery interns with a junior college degree or above and an academic duration of at least 3 years; completion of basic obstetric theory courses; age ≥18 years; and voluntary provision of written informed consent. The exclusion criterion was inability to complete the training cycle because of medical leave or other reasons. All participants were female. Because there were no male participants, the influence of sex on the intervention effect could not be evaluated.

Baseline characteristics, including age, pre-training theoretical knowledge, and prior simulation exposure, were comparable between the two groups. However, because of the sequential cohort design, potential time-related confounding factors, such as improvements in instructor experience, curricular refinement, and increasing familiarity with simulation-based education, may exist and should be considered when interpreting the findings.

Ethics approval and informed consent

This study was reviewed and approved by the Clinical Research Ethics Committee of the Second Affiliated Hospital of Wenzhou Medical University (approval number: 2025-K-473–02) and completed the filing in the National Medical Research Registration and Filing Information System (Filing Number: MR-33-26-008947). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki.

Written informed consent was obtained from all participants before participation in the study. For any identifiable information, images, or clinical data included in this manuscript, informed consent for publication was obtained from the participants or their legal guardians.

Method details

Theoretical framework

The core mechanism of Thorndike’s “trial and error” theory. The “trial and error” theory is a theory of the human learning process proposed by the famous American psychologist Thorndike. This theory emphasizes that learning is a process of “attempting, making mistakes, adjusting, and gradually establishing the connection between stimuli and responses”.30 Based on this theory, the present study combined error exposure with forward-looking prevention strategies to form a four-stage preventive trial-and-error teaching model: creating error cases, guiding error correction, discussing prevention, and evaluating feedback.

Study design and sample size

Power analysis was performed using G∗Power 3.1 software. A moderate effect size (d = 0.5), α = 0.05 (two-sided test), and β = 0.20 were preset. The statistical power of the current sample size, consisting of 43 participants in the control group and 46 participants in the Intervention group, was 81.2%. The actual effect size was Cohen’s d = 0.58. See Table S1 for the input parameters and Table S2 for the analysis results.

High-fidelity simulation intervention

The high-fidelity simulation system used in the study was Laerdal SimMom, which supports dynamic changes in physiological parameters. Combined with clinical practice, a database of error cases for breech delivery skills was established through a standardized process (Table 1): (1) clinical experts identified and validated common breech delivery errors; (2) errors were categorized by severity and learning objective; and (3) scenarios were scripted into standardized teaching modules with clear triggers and learning outcomes.

The teacher’s role was defined as a guide and facilitator rather than a direct demonstrator. During the error exposure phase, the teacher controlled scenario progression, monitored student performance, and prompted reflection through open-ended questions without immediately correcting errors. A standardized teaching procedure was developed using written lesson plans, teaching manuals, and the supporting software of the high-fidelity simulator.

The four-stage preventive trial-and-error process consisted of creating error cases, guiding interns to correct errors, discussing preventive measures, and evaluating, summarizing, and providing feedback.

Teaching implementation process

Creating error cases

Before class, error cases were preset using the high-fidelity manikin system, and videos were recorded for Cases 02, 03, and 04. After the class started, interns watched the online videos. Subsequently, the high-fidelity simulation system was used to run the preset offline error case. In Case 01, the early warning of umbilical cord prolapse was ignored, while parameters such as fetal heart rate and uterine contraction frequency were displayed. One intern was invited to perform breech presentation delivery skills during the case, allowing errors to be exposed during the trial-and-error process. After an incorrect operation, fetal heart rate monitoring showed fetal distress in real time, with the heart rate decreasing to 80 beats per minute.

Guiding interns to correct errors

Interns were assigned observation tasks individually and observed the details of the cases from different knowledge dimensions. They established a direct connection between incorrect operations and negative outcomes and observed the cases in depth to understand the application of the relevant knowledge.

Discussing preventive measures

The brainstorming method was used,31 with a predefined time limit for discussion. Interns were guided to analyze the cases, identify the direct causes (such as inadequate procedural proficiency) and potential causes (such as knowledge gaps) of the errors,32and use a fishbone diagram33 to trace their root causes. For example, educational factors were considered in relation to insufficient training, whereas system-related factors included the lack of standardized operating procedures. Through this process, interns were encouraged to move beyond identifying immediate errors and to consider the underlying factors contributing to their occurrence and potential strategies for prevention.

Evaluation, summary, and feedback

The teacher acts as a guide and assistant and jointly discusses the cases with the interns.34,35,36 Then, run the manikin system again. After the teacher demonstrates correctly, random run cases such as No. 02, 03, and 04. Each intern practices one by one, and the teacher and students provide on-site feedback. The teacher gives an impression evaluation score in class (accounting for 5% of the out-of-department examination score). In the second week, conduct the second class according to the above four stages. Select Cases No. 06 and 07 as the online observation videos, and Case No. 05 as the offline “trial and error” case. In the third week, conduct the third class. Select Cases No. 09 and 10 as the online observation videos, and Case No. 08 as the offline “trial and error” case. The time allocation of each class is shown in Table S3. The content arrangement of the three classes is shown in Figure 1.

Evaluation indicators

Breech delivery skill mastery

The full score was 100 points. Evaluations in the first, second, and third skill training classes each accounted for 5%, while the assessment of breech presentation delivery skills in the fourth week accounted for 85%.

Training transfer effect

Use the Training Transfer Effect Scale studied by Lin, W., et al. and others.29,37 The content includes six items: after receiving the training, the interns’ knowledge reserve and skill level, the application of the knowledge and skills learned in the training to their work, work enthusiasm, confidence in accepting new work tasks, improvement of work performance, and improvement of work behavior. It adopts a 5-point Likert scale, with 1 point for “strongly disagree”, 2 points for “disagree”, 3 points for “neutral”, 4 points for “agree”, and 5 points for “strongly agree”. The total score ranges from 6 to 30 points, and the effective recovery rate is 100%. The α coefficient of this scale is 0.694.

Quantification and statistical analysis

One week after completion of the high-fidelity simulation training for breech delivery skills, all participating midwifery interns underwent a standardized assessment of breech delivery skills and completed the Training Transfer Effect Scale, with a 100% questionnaire recovery rate. The final analysis included 89 midwifery interns, comprising 46 participants in the Intervention group and 43 participants in the control group. Here, n represents the number of midwifery interns. Statistical analyses were performed using IBM SPSS Statistics version 22.0 (IBM Corp.). Continuous variables were summarized as mean ± standard deviation (SD) and compared between the two groups using independent-samples t tests. Cohen’s d was calculated to describe the standardized effect size of between-group differences. A two-sided p value <0.05 was considered statistically significant. Statistical results are presented in Tables 2 and 3.

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2026.117490.

Supplemental information

Document S1. Tables S1–S3
mmc1.pdf (89.7KB, pdf)

References

  • 1.Tsakiridis I., Mamopoulos A., Athanasiadis A., Dagklis T. Management of Breech Presentation: A Comparison of Four National Evidence-Based Guidelines. Am. J. Perinatol. 2020;37:1102–1109. doi: 10.1055/s-0039-1692391. [DOI] [PubMed] [Google Scholar]
  • 2.Wangberg Nordborg J., Svanberg T., Strandell A., Carlsson Y. Term breech presentation-Intended cesarean section versus intended vaginal delivery-A systematic review and meta-analysis. Acta Obstet. Gynecol. Scand. 2022;101:564–576. doi: 10.1111/aogs.14333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hotton E., O'Brien S., Draycott T.J. Skills training for operative vaginal birth. Best Pract. Res. Clin. Obstet. Gynaecol. 2019;56:11–22. doi: 10.1016/j.bpobgyn.2018.10.001. [DOI] [PubMed] [Google Scholar]
  • 4.Morris S., Geraghty S., Sundin D. Consensus-based recommendations for the care of women with a breech presenting fetus. Midwifery. 2024;130 doi: 10.1016/j.midw.2024.103916. [DOI] [PubMed] [Google Scholar]
  • 5.Lei Y.Y., Zhu L., Sa Y.T.R., Cui X.S. Effects of high-fidelity simulation teaching on nursing students' knowledge, professional skills and clinical ability: A meta-analysis and systematic review. Nurse Educ. Pract. 2022;60 doi: 10.1016/j.nepr.2022.103306. [DOI] [PubMed] [Google Scholar]
  • 6.Takhdat K., Eddabbah M., El Hamzaoui H., Lamtali S., El Adib A.R. High-Fidelity Simulation Effects on Cardiopulmonary Resuscitation Self-Efficacy and Knowledge Retention in Undergraduate Nursing Students: A Two-Group, Experimental, Longitudinal Pilot Study. Nurs. Educ. Perspect. 2022;43:E118–E120. doi: 10.1097/01.NEP.0000000000000984. [DOI] [PubMed] [Google Scholar]
  • 7.Akalin A., Sahin S. The impact of high-fidelity simulation on knowledge, critical thinking, and clinical decision-making for the management of pre-eclampsia. Int. J. Gynaecol. Obstet. 2020;150:354–360. doi: 10.1002/ijgo.13243. [DOI] [PubMed] [Google Scholar]
  • 8.Jiang M.H., Dou L.W., Dong B., Zhang M., Li Y.P., Lin C.X. Development and implementation of a high-fidelity simulation training course for medical and nursing collaboration based on the Fink integrated course design model. Front. Med. 2024;11 doi: 10.3389/fmed.2024.1286582. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Zheng J., Lapu R., Khalid H. Integrating High-Fidelity Simulation into a Medical Cardiovascular Physiology Curriculum. Adv. Med. Educ. Pract. 2020;11:41–50. doi: 10.2147/AMEP.S230084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Zhu Y.M., Chwyl B., Fawcett S., Yang F., Warkentin H., Ghosh S., Debenham B., Duimering A., Al Balushi M. Design and Implementation of a Multidisciplinary High-Fidelity Simulation Course for the Management of Malignant Spinal Cord Compression. J. Cancer Educ. 2023;38:1939–1947. doi: 10.1007/s13187-023-02364-y. [DOI] [PubMed] [Google Scholar]
  • 11.Dyre L., Tabor A., Ringsted C., Tolsgaard M.G. Imperfect practice makes perfect: error management training improves transfer of learning. Med. Educ. 2017;51:196–206. doi: 10.1111/medu.13208. [DOI] [PubMed] [Google Scholar]
  • 12.Meritet D., Gorman M.E., Townsend K.L., Chappell P., Kelly L., Russell D.S. Investigating the Effects of Error Management Training versus Error Avoidance Training on the Performance of Veterinary Students Learning Blood Smear Analysis. J. Vet. Med. Educ. 2021;48:319–329. doi: 10.3138/jvme.2019-0055. [DOI] [PubMed] [Google Scholar]
  • 13.Lund S., Cook D.A., Shaikh N., Shagu A., Nelson M., Rivera M. Video feedback with error-focused or correct-focused examples in surgical skills distance learning: A randomized trial. Surgery. 2022;172:1346–1351. doi: 10.1016/j.surg.2022.06.035. [DOI] [PubMed] [Google Scholar]
  • 14.Mera Y., Rodriguez G., Marin-Garcia E. Unraveling the benefits of experiencing errors during learning: Definition, modulating factors, and explanatory theories. Psychon. Bull. Rev. 2022;29:753–765. doi: 10.3758/s13423-021-02022-8. [DOI] [PubMed] [Google Scholar]
  • 15.Klein M., Otto B., Fischer M.R., Stark R. Fostering medical students' clinical reasoning by learning from errors in clinical case vignettes: effects and conditions of additional prompting procedures to foster self-explanations. Adv. Health Sci. Educ. Theory Pract. 2019;24:331–351. doi: 10.1007/s10459-018-09870-5. [DOI] [PubMed] [Google Scholar]
  • 16.Snyder A.M., Hartwell J.R. Development and Evaluation of an Experiential Career Planning Assignment to Train Students to Assess Organizational Fit. J. Vet. Med. Educ. 2024;51:27–37. doi: 10.3138/jvme-2022-0095. [DOI] [PubMed] [Google Scholar]
  • 17.Soncini A., Matteucci M.C., Tomasetto C., Butera F. Supportive error feedback fosters students' adaptive reactions towards errors: Evidence from a targeted online intervention with Italian middle school students. Br. J. Educ. Psychol. 2025;95:92–106. doi: 10.1111/bjep.12679. [DOI] [PubMed] [Google Scholar]
  • 18.Brydges R., Carnahan H., Rose D., Rose L., Dubrowski A. Coordinating progressive levels of simulation fidelity to maximize educational benefit. Acad. Med. 2010;85:806–812. doi: 10.1097/ACM.0b013e3181d7aabd. [DOI] [PubMed] [Google Scholar]
  • 19.Roads B.D., Xu B., Robinson J.K., Tanaka J.W. The easy-to-hard training advantage with real-world medical images. Cogn. Res. Princ. Implic. 2018;3:38. doi: 10.1186/s41235-018-0131-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Oliveira Silva G., Oliveira F.S.E., Coelho A.S.G., Fonseca L.M.M., Vieira F.V.M., Campbell S.H., Aredes N.D.A. Influence of simulation design on stress, anxiety and self-confidence of nursing students: Systematic review with meta-analysis. J. Clin. Nurs. 2023;32:5668–5692. doi: 10.1111/jocn.16681. [DOI] [PubMed] [Google Scholar]
  • 21.Zhu W., Zhu C., Min H., Li L., Wang X., Wu J., Zhu X., Gu C. Status of the midwifery workforce and childbirth services and the impact of midwife staffing on birth outcomes in China: a multicentre cross-sectional study. BMJ Open. 2024;14 doi: 10.1136/bmjopen-2023-082527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Soemantri D., Greviana N., Findyartini A., Azzahra T.B., Suryoadji K.A., Mustika R., Felaza E. To obey or not to obey - Medical students' response towards professional dilemmas in a hierarchical and collectivist culture. PLoS One. 2021;16 doi: 10.1371/journal.pone.0261828. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.McClintock A.H., Fainstad T. Growth, Engagement, and Belonging in the Clinical Learning Environment: the Role of Psychological Safety and the Work Ahead. J. Gen. Intern. Med. 2022;37:2291–2296. doi: 10.1007/s11606-022-07493-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Pugh C.M., Law K.E., Cohen E.R., D'Angelo A.L.D., Greenberg J.A., Greenberg C.C., Wiegmann D.A. Use of error management theory to quantify and characterize residents' error recovery strategies. Am. J. Surg. 2020;219:214–220. doi: 10.1016/j.amjsurg.2019.11.013. [DOI] [PubMed] [Google Scholar]
  • 25.Wiegmann D.A., Wood L.J., Cohen T.N., Shappell S.A. Understanding the “Swiss Cheese Model” and Its Application to Patient Safety. J. Patient Saf. 2022;18:119–123. doi: 10.1097/PTS.0000000000000810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Zeng Q., Wang K., Liu W.X., Zeng J.Z., Li X.L., Zhang Q.F., Ren S.Q., Xu W.M. Efficacy of high-fidelity simulation in advanced life support training: a systematic review and meta-analysis of randomized controlled trials. BMC Med. Educ. 2023;23:664. doi: 10.1186/s12909-023-04654-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Bilal K.H., Gibson J., Kim D., Levi A.W., Nassar A., Oen H., Robila V., Saieg M.T., Shi Q., Torous V.F., et al. Leveraging large language models to enhance cytopathology: Opportunities, challenges, and future directions; a practical review from the ASC Clinical Practice Committee. Cancer Cytopathol. 2026;134 doi: 10.1002/cncy.70134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Burgess A., van Diggele C., Roberts C., Mellis C. Feedback in the clinical setting. BMC Med. Educ. 2020;20:460. doi: 10.1186/s12909-020-02280-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Lin W., Zhang P., Li Y., Li S., Fang Z., Zhang H. Enhancing training transfer among stroke specialist nurses: Insights from latent profile analysis. Nurse Educ. Pract. 2024;81 doi: 10.1016/j.nepr.2024.104180. [DOI] [PubMed] [Google Scholar]
  • 30.Athalye V.R., Santos F.J., Carmena J.M., Costa R.M. Evidence for a neural law of effect. Science. 2018;359:1024–1029. doi: 10.1126/science.aao6058. [DOI] [PubMed] [Google Scholar]
  • 31.Poder T.G., Maltais S. Systemic analysis of medication administration omission errors in a tertiary-care hospital in Quebec. Health Inf. Manag. 2020;49:99–107. doi: 10.1177/1833358318781099. [DOI] [PubMed] [Google Scholar]
  • 32.Patel R.H., Goldin J. StatPearls. StatPearls Publishing; Treasure Island (FL): 2026. Medical Error Prevention and Root Cause Analysis. [PubMed] [Google Scholar]
  • 33.Azizi S., Siddiqui F., Iqbal I. Changing health care culture: a prerequisite to improving patient safety. Ther. Clin. Risk Manag. 2017;13:623–624. doi: 10.2147/TCRM.S138896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Hee J.M., Yap H.W., Ong Z.X., Quek S.Q.M., Toh Y.P., Mason S., Krishna L.K.R. Understanding the Mentoring Environment Through Thematic Analysis of the Learning Environment in Medical Education: a Systematic Review. J. Gen. Intern. Med. 2019;34:2190–2199. doi: 10.1007/s11606-019-05000-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Sartania N., Sneddon S., Boyle J.G., McQuarrie E., de Koning H.P. Increasing Collaborative Discussion in Case-Based Learning Improves Student Engagement and Knowledge Acquisition. Med. Sci. Educ. 2022;32:1055–1064. doi: 10.1007/s40670-022-01614-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Yang Y., Guo Z., Guo X., Zhao Y. Enhancing postpartum hemorrhage training by integrating case-problem based learning with simulation: a quasi experimental study. J. Obstet. Gynaecol. 2025;45 doi: 10.1080/01443615.2024.2443617. [DOI] [PubMed] [Google Scholar]
  • 37.Osman A.M., Jaffe P.I., Ng N.F., Kerlan K.R., Schafer R.J. Transfer of learning: Analysis of dose-response functions from a large-scale, online, cognitive training dataset. PLoS One. 2023;18 doi: 10.1371/journal.pone.0281095. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Tables S1–S3
mmc1.pdf (89.7KB, pdf)

Data Availability Statement

  • •

    Data reported in this study will be shared by the lead contact upon request.

  • •

    This study does not report original code.

  • •

    Any additional information required to reanalyze the data reported in this study are available from the lead contact upon request.


Articles from iScience are provided here courtesy of Elsevier

RESOURCES