Barrier to Healthcare Access Faced by Indigenous Women in the Guatemalan Highland
Bibliographic record
Abstract
Utilizing the Framework Method, qualitative research determined the effects of potential barriers to healthcare access faced by 15 self-selected, consenting Indigenous women living in three different communities in the Guatemalan highlands. The women were actively involved in the nutritional recuperation program of the Community Organization, a non-profit clinic. Data collection involved recorded interviews based on a questionnaire designed to ensure culture competency. Responses were grouped into categories based on their relation to potential barriers to healthcare access and were then coded based on impacts on healthcare seeking behaviours. Intercoder reliability was measured and negotiated agreement of results was conducted to reach 100% agreement. Analyses of coded responses compared results between communities and between available sectors of healthcare (folk, public, and non-profit). Inductive reasoning was used to determine the effect of beliefs related to illness on healthcare seeking behaviour. Analyses showed significant differences in the impact of geographical barriers to healthcare access among communities across public and non-profit sectors of healthcare, p < 0.05, and demonstrated categorization of disease states and influence of beliefs related to illness on healthcare seeking behaviour. Results demonstrated a hierarchy of barriers, with barriers such as cost, perceived quality of care, trust of medical provider, and available time only showing a negative effect once the barrier of geography was overcome. Despite the sample bias, these results give insight into factors affecting healthcare seeking behaviours that could contribute to the low utilization of healthcare seen in this population.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".