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Record W4411426729 · doi:10.1016/j.ard.2025.05.359

OP0357-HPR A Syndemic Care Model for the Management of Rheumatic and Musculoskeletal Diseases in Indigenous Maya-Yucatec Populations: A Mixed-Methods Study

2025· article· en· W4411426729 on OpenAlexaff
Cinthya Cadena-Trejo, E. Motte Garcia, Alfonso Gastelum‐Strozzi, Adalberto Loyola‐Sánchez, N. Facio-Escalona, V. Fernández-García, J.F. Moctezuma, Arturo Recabarren Lozada, David López Aguilar, Kenia Nayrobi López-Herrera, P. Loeza-Magaña, Hugo Laviada‐Molina, Ingris Peláez‐Ballestas

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsAlberta HealthAlberta Health Services
Fundersnot available
KeywordsMedicineSyndemicIndigenousFamily medicineTraditional medicineHuman immunodeficiency virus (HIV)

Abstract

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Background: Rheumatic and Musculoskeletal Diseases (RMSDs) in the Maya-Yucatec population generate a syndemic when interacting with the most common Non-Communicable Chronic Diseases (NCDs), such as diabetes, hypertension, and obesity, leading to disability and a decrease in quality of life. A syndemic refers to the negative synergy between two or more diseases within an unfavorable context, resulting in worst health outcomes. The Syndemic Care Model (SCM) is an intervention designed to address the syndemia between RMSDs and NCDs, considering the structural factors that hinder adequate healthcare for marginalized populations. Using a Community-Based Participatory Research (CBPR) strategy, partnerships are established between researchers, communities, and authorities to address specific health needs. Objectives: To co-design and Syndemic Care Model in collaboration with the community to address health needs related to the syndemic produced by the interaction between RMSDs and NCDs. Methods: A parallel-convergent mixed-methods study following the phases of CBPR in three Maya-Yucatec communities. The quantitative component was a cross-sectional study, and the qualitative component was an ethnographic study. Phase 1: Census using the COPCORD methodology to identify RMSDs/NCDs. The following questionnaires were administered: HAQ-Di, EuroQoL 5D-3L, and service utilization, all validated in Maya and Spanish. Semi-structured interviews with patients and healthcare professionals and ethnographic records/field notes were made. Phase 2: Identifying and prioritizing health needs with community leaders and patients. Quantitative analysis: descriptive analysis of continuous and categorical variables using RStudio 4.4.2. Qualitative analysis: inductive coding using Atlas.ti and thematic analysis. The project was approved by the Ethics and Research Committee of the General Hospital of Mexico (DI/23/404-B/05/22). Results: A total of 508 people participated in the census, 69% were women, with an average age of 49 years; 53% reported being homemakers. 42% of participants were from Xkalakdzonot, 33% from Xcopteil, and the rest from Yaxunah. The prevalence of RMSDs was 8%. Eleven patients with RMSDs and six healthcare professionals were interviewed. Two focus groups, multiple community assemblies, and meetings with health and municipal authorities were conducted to strengthen collaborations. Barriers and facilitators to accessing healthcare services were identified, and the health needs expressed by the communities were prioritized (see Table 1). Based on the findings, the elements of the SCM were established, including, 1) building connections between local and regional healthcare systems to address NCDs, 2) collaborating with the municipality for the provision of medications and transportation, 3) caring for RMSDs through rheumatology and rehabilitation consultations in each community, and 4) educating to address and prevent NCDs complications (see Figure 1). Conclusion: We co-designed a culturally sensitive SCM through the implementation of a mixed methods research study that allowed us to identify and prioritize the health needs of the participant Mayan Communities. Therefore, we anticipate that the future implementation of this model will result in improved health outcomes for those living with RMSDs/NCDs. REFERENCES: [1] Singer M, Bulled N, Ostrach B, Mendenhall E. Syndemics and the biosocial conception of health. The Lancet. 2017;389(10072):941-950. [2] Ramírez-Flores MF, Cadena-Trejo C, Motte-García E, Juárez-Cruz ID, Fernández-García MF, Gastelum-Strozzi A, et al. A Mixed-Methods Systematic Review on Syndemics in Rheumatology. J Clin Rheumatol. 2023 Apr 1;29(3):113-1. Acknowledgements: Partial funding from the Marista University of Merida. CONAHCYT (CVU 671018 and CVU1145201). Disclosure of Interests: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.412
Teacher spread0.382 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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