Influence of emotions on clinical performance in acute care: A scoping review
Bibliographic record
Abstract
Acute care is a high stake, emotionally charged environment. Although emotions are increasingly recognized as integral to various aspects of healthcare, research examining how they influence and interact with clinical performance in acute care settings remains relatively limited. This scoping review aims to summarize relevant empirical research on the influence of emotions on clinical performance in acute care settings. The following databases were searched by a health sciences librarian: Medline and Medline in Process, Embase Classic and Embase, Cochrane's CENTRAL, APA PsycINFO, CINAHL, and ERIC, from inception to June 2024. Empirical research in English related to the effect of emotions on clinical performance in acute care settings were included. The screening was conducted in duplicate independently, and data extraction was done by the lead author and reviewed by a second author. Among 6430 references assessed, 22 studies were analyzed. Three themes were identified based on the research setting: simulated/educational acute care settings, real-world acute care settings, and end-of-life care settings. Overall, negative emotions, most commonly stress, were inversely correlated with clinical performance in some simulated or educational settings and discouraged patient contact in real clinical settings, while positive emotions encouraged more comprehensive care. Experiencing fear and uncertainty led to more cautious care decisions, and negative emotions associated with patient's families were prevalent in end-of-life care. Emotions had varying effects on clinical performance and decision-making in acute care settings, depending on the types of emotions and the clinical contexts. More research is needed to find strategies to help clinicians manage those emotions.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".