Reaching the unreachable: a mixed-method evaluation of multidimensional healthcare model addressing the healthcare service gaps in hard-to-reach Northern riverine Bangladesh
Bibliographic record
Abstract
BACKGROUND: Hard-to-reach riverine communities of northern Bangladesh face unique challenges in healthcare services. Friendship, an international social purpose organization, implemented a 3-tier healthcare model addressing these unique challenges over the past 20 years. This study evaluated Friendship's 3-tier healthcare model in the northern riverine area, assessing service-seeking practices, experiences, stakeholders' perceptions, and cost benefits for beneficiaries. METHODS: A concurrent mixed-method approach was employed, including desk reviews, a cross-sectional quantitative survey, facility mapping, and qualitative interviews with service recipients, community representatives, healthcare providers, and health managers. Data were collected from five hard-to-reach riverine sub-districts across the Kurigram, Gaibandha, Bogura, Sirajganj, and Jamalpur districts of Bangladesh between April 2022 and July 2023. Data analysis followed major thematic domains for a comprehensive and complementary understanding. RESULTS: A significant proportion (43.0%) of survey participants had no formal education, were aged 18-35 (57.5%), and earned less than 1,620 USD yearly (66.6%). Friendship's healthcare services at the doorstep through satellite clinics and Female Community Medic Aides were widely accepted and preferred within the community for convenience, affordability (0.05-0.09 USD service charges), and superior quality, particularly the specialized treatments available on the hospital ships. CONCLUSION: Friendship's 3-tier healthcare model made the accessibility and affordability of primary healthcare. Upon implementing a robust referral mechanism, continuing collaboration with the Government of Bangladesh, and expanding community awareness sessions to include topics such as mental health and disaster response, this model has the potential to be effective in similar settings in Bangladesh and other developing countries, as well as during emergency responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".