Making the post-MDG global health goals relevant for highly inequitable societies: findings from a consultation with marginalized populations in Guatemala
Bibliographic record
Abstract
INTRODUCTION: The United Nations presented a set of Millennium Development Goals that aimed to improve social and economic development and eradicate poverty by 2015. Most low and middle-income countries will not meet these goals and today there is a need to set new development agenda, especially when it comes to health. The paper presents the findings from a community consultation process carried out within the Goals and Governance for Global Health (GO4Health) research consortium in Guatemala, which aims to identify community needs and expectations around public policies and health services. METHODS: Through a participative and open consultation process with experts, civil society organizations and members of the research team, the municipalities of Tectitan and Santa Maria Nebaj were selected. A community consultation process was undertaken with community members and community leaders. Group discussions and in-depth interviews were conducted and later analyzed using thematic analysis, a qualitative method that can be used to analyze data in a way that allows for the identification of recurrent patterns that can be grouped into categories and themes, was used. FINDINGS: Following the Go4Health framework's domains for understanding health-related needs, the five themes identified were health, social determinants of health, essential health needs and their provision, roles and responsibilities of relevant stakeholders and community participation in decision-making. Participants reported high levels of discrimination related to ethnicity, to being poor and to living in rural areas. Ethnicity played a major role in how community members feel they are cared for in the health system. CONCLUSION: Achieving health goals in a context of deep-rooted inequality and marginalization requires going beyond the simple expansion of health services and working with developing trusting relationships between health service providers and community members. Involving community members in decision-making processes that shape policies will contribute to a larger process of community empowerment and democratization. Still, findings from the region show that tackling these issues may prove complicated and require going beyond the health system, as this lack of trust and discrimination has permeated to all public policies that deal with indigenous and rural populations.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".