Interventions for improving teamwork in intrapartum care: a systematic review of randomised controlled trials
Bibliographic record
Abstract
BACKGROUND: The labour and delivery environment relies heavily on interdisciplinary collaboration from anaesthesiologists, obstetricians and nurses or midwives to deliver optimal patient care. A large number of adverse events in obstetrics are associated with failure in communication and teamwork among team members, with substantive consequences. The objective of this study is to perform a systematic review of interventions aimed at improving teamwork in obstetrics. METHODS: This systematic review identified and assessed randomised controlled trials (RCTs) of interventions aimed at improving teamwork among interdisciplinary teams in obstetrical care. Medline, CENTRAL, CINAHL and Embase were searched for studies evaluating one of: patient outcomes, team performance or processes of clinical efficiency. Identified citations were reviewed in duplicate for eligibility. RESULTS: Nine RCTs met the inclusion criteria; five of these RCTs were conducted under simulated clinical environments. Simulation-based teamwork training interventions were the most represented (n=7 studies, 3047 healthcare providers (HCPs), 107 782 births), followed by checklists (n=1 study, 136 HCPs) and an electronic-based decision support tool (n=1 study, 296 HCPs). Simulation-based teamwork training was found to improve team performance in 100% of relevant studies (3 of 3 studies assessing team performance) and patient morbidity in 75% of relevant studies (3 of 4 studies assessing patient morbidity). However, no direct mortality benefit was identified among all the studies reviewed. Studies were assessed to be of low-moderate quality and had significant limitations in their study designs. CONCLUSION: While the evidence is still limited and from low to moderate quality RCTs, simulation-based teamwork interventions appear to improve team performance and patient morbidity in labour and delivery care. PROSPERO TRIAL REGISTRATION NUMBER: CRD42018090452.
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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 | 0.037 | 0.128 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".