Notice bibliographique
Résumé
Cultural behaviors have important implications for human health. Culture, a socially transmitted system of shared knowledge, beliefs and/or practices that varies across groups, and individuals within those groups, has been a critical mode of adaptation throughout the history of our species [1]. Socioeconomic status, gender, religion and moral values all play into how individuals experience, conceptualize and react to their world, and therefore general understandings of cultural groups are insufficient for grasping a patient’s unique experience with health and illnesses [2, 3]. Additionally, structural inequalities and political economy play a critical, and often overlooked, role in health and disease [4]. Understanding how behaviors are rooted in an individual’s unique cultural experience and as a response to social pressures can better equip medical professionals with the context, skills and empathy necessary for holistic care [2]. Healthcare providers can improve individual outcomes by thoroughly factoring in life experiences as part of understanding an individual’s health and treating their illnesses. The use of a ‘mini-ethnography’ can help healthcare providers understand how identity, interpretation of illness and the moral values of patients factor into building a trusting relationship that considers the patient’s life experiences into treatment plans [3]. Table 1 summarizes this approach. Kleinman and Benson’s approach to conducting a ‘mini-ethnography’ with every patient in order to best incorporate a patient’s culture into treatment plans [3] Kleinman and Benson’s approach to conducting a ‘mini-ethnography’ with every patient in order to best incorporate a patient’s culture into treatment plans [3] In rural Bolivia, children of mothers with higher indices of local ecological knowledge (LEK) had reduced inflammation, taller height, and less hookworm infections than children of mothers with lower indices of LEK [5, 6]. The Acholi people of Uganda have several cultural models for understanding and responding to disease outbreaks that were employed during the 2000 Ebola outbreak [7]. Acholi cultural practices related to gemo, or an epidemic outbreak, limit the spread of infectious diseases that may have occurred through traditional funerary practices, such as the washing and touching of deceased bodies [7]. Both examples highlight a need for understanding Indigenous knowledge systems as they relate to health and in responding to disease. Understanding how social pressures, such as racism and discrimination, manifest biologically is critical in understanding how cultural behavior relates to health. In a sample of diverse pregnant women in New Zealand, those that experienced ethnic discrimination had high cortisol levels and their infants higher cortisol reactivity, suggesting a transgenerational effect of discrimination [8]. Margarita Hernandez is supported by National Science Foundation Grant No. DGE1255832. Conflict of interest: None declared.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».