Capturing the Complexity of Acute Stress in the Health Professions: A Review of Methods for Measuring Stress and Considerations for Moving Forward
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,056 | 0,103 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,005 |
| Bibliométrie | 0,031 | 0,026 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,009 | 0,011 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».