Reducing syndemics of non-communicable chronic diseases in Mayan Indigenous population through community-based participatory research: a mixed-methods study protocol
Bibliographic record
Abstract
BACKGROUND: Indigenous Mayan-Yucatecan communities in Mexico have a high prevalence of chronic non-communicable diseases (NCDs) such as diabetes, hypertension, obesity and rheumatic diseases (RMDs). According to the syndemic theory, these diseases combined with social, economic and cultural factors affect the quality of life. The aim of this protocol is to describe the methodological process to create, implement and evaluate a Syndemic-Based Care Model (SCM), using a Community Based-Participatory Research (CBPR) strategy in three Mayan-Yucatecan communities. METHODS AND ANALYSIS: This is a convergent mixed-methods protocol. The quantitative component is a before-after study, and the qualitative component is an ethnographic study. The intervention will be a SCM co-constructed with Mayan communities based on their particular needs and aiming for reduction of the negative impact of NCD/RMD through a multidisciplinary approach. We will follow four phases of the CBPR: (1) situational analysis, through community censuses and semistructured interviews to understand the prevalence of NCDs and the syndemics in these communities; (2) co-construction of the elements of a SCM based on the health priorities identified by researchers, community members and healthcare workers; (3) implementation of this SCM and (4) evaluation of the SCM through (1) statistical analyses involving the construction of a syndemic index through stepwise logistic regression of the normalised and standardised key clinical, social and economic variables; interval and ratio variables will be normalised by their z-score and categorical variables will be one-hot encoded; similarity and social networks analysis with clustering to identify syndemic subpopulations; and cost-effectiveness and cost-utility analyses using Markov modelling and (2) narrative and thematic qualitative analysis of the SCM's implementation and impact on community members' health, function and quality of life. ETHICS AND DISSEMINATION: Research ethics boards of participant institutions approved this research protocol. This project will be presented to municipal authorities, community meetings and community leaders for observation and acceptance. For people who wish to participate, informed consent will be provided written and verbally in Spanish or Mayan-Yucatecan according to the participant preferences, and it can be signed by either autograph or fingerprint. The results of this research will be disseminated to various groups: (a) local and regional authorities of the Mexican health system and municipal authorities; (b) the participating communities will be informed in an assembly of the results and (c) academic dissemination will be done through publications in public science journals and institutional press releases and will also be presented at national and international congresses or symposia.
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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 | 0.068 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".