Abuse and discrimination towards indigenous people in public health care facilities: experiences from rural Guatemala
Bibliographic record
Abstract
BACKGROUND: Health inequalities disproportionally affect indigenous people in Guatemala. Previous studies have noted that the disadvantageous situation of indigenous people is the result of complex and structural elements such as social exclusion, racism and discrimination. These elements need to be addressed in order to tackle the social determinants of health. This research was part of a larger participatory collaboration between Centro de Estudios para la Equidad y Gobernanza en los Servicios de Salud (CEGSS) and community based organizations aiming to implement social accountability in rural indigenous municipalities of Guatemala. Discrimination while seeking health care services in public facilities was ranked among the top three problems by communities and that should be addressed in the social accountability intervention. This study aimed to understand and categorize the episodes of discrimination as reported by indigenous communities. METHODS: A participatory approach was used, involving CEGSS's researchers and field staff and community leaders. One focus group in one rural village of 13 different municipalities was implemented. Focus groups were aimed at identifying instances of mistreatment in health care services and documenting the account of those who were affected or who witnessed them. All of the 132 obtained episodes were transcribed and scrutinized using a thematic analysis. RESULTS: Episodes described by participants ranged from indifference to violence (psychological, symbolic, and physical), including coercion, mockery, deception and racism. Different expressions of discrimination and mistreatment associated to poverty, language barriers, gender, ethnicity and social class were narrated by participants. CONCLUSIONS: Addressing mistreatment in public health settings will involve tackling the prevalent forms of discrimination, including racism. This will likely require profound, complex and sustained interventions at the programmatic and policy levels beyond the strict realm of public health services. Future studies should assess the magnitude of the occurrence of episodes of maltreatment and racism within indigenous areas and also explore the providers' perceptions about the problem.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".