Capturing the Complexity of Acute Stress in the Health Professions: A Review of Methods for Measuring Stress and Considerations for Moving Forward
Bibliographic record
Abstract
Purpose: Acute stress in health care professionals can lead to serious consequences both for the professional and the patient, thereby justifying the pursuit of understanding of stress in this environment. Previous studies of stress in health care have largely focused on one dimension of the stress phenomenon in isolation, most often in the simulated setting. This review sought to summarize the current landscape and propose a multidimensional methodology for capturing and studying the complexity of acute stress in health care professionals in the natural setting. Method: A scoping review of the literature was performed to map the existing literature in the study of stress in health professionals as well as to identify research gaps. Studies were identified from the databases MEDLINE, Embase, and PsychINFO and the bibliographies of important studies and pertinent texts up to and including 2015. Identified studies were charted and categorized by methodology used to measure acute stress. The differing methods used were then critically analyzed to identify strengths and limitations. Results: The major methodological approaches to studying stress that were identified were physiologic, cognitive, affective, and sociocultural. Measures of physiologic variables attributed to the stress response were the most commonly collected in the literature. These included measures of the autonomic nervous system (heart rate, blood pressure, heart rate variability, galvanic skin response, alpha-amylase) and of the hypothalamic–pituitary axis (cortisol). While sensitive, many physiologic measures lacked specificity and context. In addition, these measures can prove difficult to collect in a meaningful way in naturalistic studies. Self-reported inventory scales such as the State–Trait–Anxiety Inventory and questionnaires were commonly used in cognitive- and affective-based methodologies. However, these methods do not always directly measure stress and are subject to limitations due to recall bias and misattribution. Furthermore, it can be difficult to differentiate cognitive and affective as the two are intimately related. Sociocultural investigations have been limited, but qualitative approaches and ethnographic research provide important insights into previously underexplored aspects of the acute stress experience, particularly relevant in the health care professions where powerful and hierarchical cultures persist. Studies that have examined both the physiologic and perceived measures of stress (cognitive or affective) have most often demonstrated little correlation. This highlights the complexity of the stress phenomenon and supports the theory that the relationship between each of the components and the resultant overall stress experience is complicated. To date, no studies have examined all four facets of the complex acute stress experience in health care professionals. Conclusions: The current literature introduces several techniques for studying acute stress in health professionals. Each approach individually lacks specificity and is limited in providing context. A methodological approach focused on triangulating various components is needed to gain better understanding of the causes, experiences, manifestations, and effects of acute stress in the health professions. As understanding of how best to represent the complexity of stress improves, challenging questions about stress in health care teams can begin to be answered more adequately.
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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 | 0.056 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".