Non-governmental Organization's (NGOs) Impact on Health Care Services in Rural Honduras: Evaluating a Short-Term Medical Mission (STMM) Utilizing a Case Study Approach
Bibliographic record
Abstract
Non-Governmental Organization’s (NGOs) Impact on Health Services in Rural Honduras: Evaluating a Short-Term Medical Mission (STMM) Utilizing a Case Study Approach Canada is a leading, international country that engages in Non-Governmental Organization (NGO)-led STMMs to low and middle-income countries for the provision of health care, education or structural development. Honduras, a chief destination country, is one of the poorest, most politically unstable in Central America. Health expenditure is among the lowest in the Americas, approximately 30.1% of the population receives no health care, and there is marked exclusion of ethnic and rural minorities. In Honduras, there is a paucity of evidence on the expectations, coordination and outcomes of STMMs. Guided by World Health Organization’s Primary Health Care (PHC) framework, an exploratory, type 2, single case study with a multiple embedded units design was used to address two research questions relating to the processes and outcomes of STMM services, and how stakeholders assess services. Eight propositions supported the data collection and a 12-day STMM involving 7 rural villages in Gracias a Dios (client n = 1120) constituted the case over three time periods (pre, during, and 10-weeks post STMM). Other data sources were key stakeholders (regional health/host officials, Honduran and Canadian health care providers). A revised, adapted Harvard evaluation tool was the principal data collection instrument. According to a review of English publications, this is the first longitudinal assessment of STMM processes, outcomes and community perspectives. Community members provided rich details regarding factors that impact their health, such as their impoverished situation and environmental challenges and risks (water, sanitation, food scarcity, poverty, and limited transportation). Diagnoses and treatments were consistent with the evidence of predominant health issues and medications provided in similar regions. Due to limited resources and/or unavailability of services STMM, clients had no opportunity to follow up on referrals. The results suggest that the existing STMM model is limited to adequately meet the needs of the people living in a rural and remote region of Honduras where poverty is extreme. The discussion situates the findings within the context of a country where, despite individuals’ constitutional right for health, political instability and multiple, intersectoral complexities challenge such right and reveal that STMM’s contributions are valued but fragmented with largely unknown outcomes. Recommendations for STMM quality and accountability, policy, education, and future research are presented.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".