Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".