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Record W4391255618 · doi:10.47941/ijcrs.1643

Social Dimensions of Antimicrobial Resistance and an Anthropological Approach: Analytic Review

2024· article· en· W4391255618 on OpenAlexaff
M. A. Jawad

Bibliographic record

VenueInternational Journal of Culture and Religious Studies · 2024
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsResistance (ecology)SociologySocial scienceBiologyEcology

Abstract

fetched live from OpenAlex

Purpose: The objective of this paper is to devise a comprehensive and layered framework to tackle antimicrobial resistance (AMR). It aims to weave together the strands of ethnomedicine, healthcare system analysis, and anthropological perspectives on illness, aligning with universal healthcare principles to encourage community participation and address the societal roots of health inequalities. The framework seeks to address AMR through a lens that views health disparities not merely as medical issues, but as complex phenomena shaped by cultural, social, and structural factors. Methodology: The research employs a single-case study design to synthesize and analyse interdisciplinary insights into AMR. This approach facilitates an in-depth understanding of how various elements, such as cultural beliefs, healthcare practices, and community dynamics, interact and influence the spread and management of AMR. By focusing on a single case, the study intends to meticulously document and interpret the nuanced interactions between these factors, providing a detailed narrative that captures the essence of the AMR challenge in a global health context. The study utilizes 66 varied references, such as journal articles, book excerpts, theses, reports, and websites, sourced from academic venues and the internet, published between 1946 and 2023. Findings: Through its investigation, the study presents a healthcare approach that marries the traditional wisdom of ethnomedicine with the precision of biomedicine, underscoring the significance of cultural competence in formulating AMR mitigation strategies. It dissects the intricate relationship between the three primary health sectors—popular, professional, and folk—and disentangles the sociocultural concepts of 'disease' and 'illness' as distinguished by medical anthropologists. The research calls for a reconceptualization of healthcare systems that goes beyond the biomedical model, advocating for an integration of the sociocultural, economic, and political dimensions that influence health and illness manifestations. Unique contribution to theory: This study's unique theoretical contribution lies in its interdisciplinary approach to health disparities and AMR. It proposes a model that balances the rigor of scientific research with the insights gleaned from traditional health practices, placing a premium on the dynamics of community involvement and the myriad influences on health. By doing so, it offers a more equitable, sustainable, and contextually relevant paradigm for health practices and policies. This paradigm shift is intended to provide actionable insights for policymakers and health practitioners, enabling them to devise strategies that are not only scientifically sound but also culturally sensitive and broadly applicable. The framework envisages a future where health interventions are tailored to the lived experiences of diverse populations, potentially transforming the landscape of global health and AMR strategy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0140.016
Science and technology studies0.0020.010
Scholarly communication0.0070.009
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.040
GPT teacher head0.413
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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