Factors influencing workload and stress during resuscitation – A scoping review
Bibliographic record
Abstract
Aim: This scoping review aimed to identify potential variables influencing healthcare provider's perceived workload or stress when performing resuscitation on patients in cardiac arrest. Methods: We searched Medline, EMBASE, PsycINFO, Cochrane, and Allied Health Literature (CINAHL) to identify studies published prior to February 1, 2024. We used a PECO format for this review: the population were healthcare providers performing resuscitation during simulated or real cardiac arrest; the exposure was the presence of any factor that could impact perceived workload or stress; and the comparator was the absence of any specific factor. Outcome variables, including self-reported questionnaires, objective and subjective measures, and any variables identified to have impact on workload and/or stress were extracted. Results: Of the initially identified 10,165 studies, 24 studies (20 RCTs, 2 quasi-experimental studies and 2 observational studies) were ultimately included. Among them, a wide variety of factors influencing perceived stress or workload were identified. High heterogeneity among studies was observed. We categorized factors into the following entities: (1) team composition and roles; (2) telemedicine; (3) workflow; (4) tools; (5) cognitive aids; (6) presence of friends and family, and (7) provider experience and exposure, representing the modifiable factors for future interventions. Conclusion: This scoping review provides an overview of factors influencing workload and stress during real and simulated cardiac arrest resuscitation. These findings highlight the need for targeted strategies to effectively manage workload and stress during resuscitation.
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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 | 0.019 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".