Bibliographic record
Abstract
OBJECTIVE: The way that health care providers feel, both within themselves and toward their patients, may influence their clinical performance and impact patient safety, yet this aspect of provider behavior has received relatively little attention. How providers feel, their emotional or affective state, may exert a significant, unintended influence on their patients, and may compromise safety. METHODS: We examined a broad literature across multiple disciplines to review the interrelationships between emotion, decision making, and behavior, and to assess their potential impact on patient safety. FINDINGS: There is abundant evidence that the emotional state of the health care provider may be influenced by factors including characteristics of the patient, ambient conditions in the health care setting, diurnal, circadian, infradian, and seasonal variables, as well as endogenous disorders of the individual provider. These influences may lead to affective biases in decision making, resulting in errors and adverse events. Clinical reasoning and judgment may be particularly susceptible to emotional influence, especially those processes that rely on intuitive judgments. CONCLUSIONS: There are many ways that the emotional state of the health care provider can influence patient care. To reduce emotional errors, the level of awareness of these factors should be raised. Emotional skills training should be incorporated into the education of health care professionals. Specifically, clinical teaching should promote more openness and discussion about the provider's feelings toward patients. Strategies should be developed to help providers identify and de-bias themselves against emotional influences that may impact care, particularly in the emotionally evocative patient. Psychiatric conditions within the provider, which may compromise patient safety, need to be promptly detected, diagnosed, and managed.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".