MAPPING HEALTH FACILITY CHARACTERISTICS AND LOCATION IN RURAL TANZANIA
Bibliographic record
Abstract
BACKGROUND: Optimization of care-seeking for pregnancy, delivery, and child health could reduce global maternal child mortality, especially in rural Africa. However, limited documentation of location, services, and staffing hamper facility-strengthening initiatives. OBJECTIVES: A mapping exercise collected basic information and global positioning system (GPS) data to characterize health facilities in two Tanzanian districts. DESIGN/METHODS: Research assistants administered a questionnaire about health facility information (i.e. maternal, newborn and child health (MNCH) services offered, staffing) verbally to in-charges at each site. GPS coordinates were recorded and delivery areas were photographed with Garmin Oregon 650 receivers. Frequencies were analysed in SPSS. Individual shapefiles for GPS waypoints and track logs were merged, producing single shapefiles of all health facilities and access routes. RESULTS: Over six days, 102 sites were visited, including three previously unlisted facilities. Four percent were hospitals, 10% health centres (HCs) and 85% dispensaries (91% public, 9% private). Most facilities were rural (86%) or mixed (9%). Ninety percent reported delivery capacity; 6% offered advanced delivery services (i.e. Caesarean sections, blood transfusions). 19,210 deliveries were reported in the preceding year (40% at hospitals, 19% at HCs, 41% at dispensaries); 46% of deliveries occurred at advanced delivery sites. On staff were a total of 20 medical officers for a doctor to population ratio of 1: 42,168. Other payroll staff included 95 clinical officers, 444 nurses, and 243 medical attendants. On day of survey, 50% of payroll staff were present; 22% of delivery-conducting sites lacked any mid-level health provider. Maps detailing precise location of all health facilities and road networks (including previously unknown roads) were produced. CONCLUSION: This mapping exercise combined technical and practical information, yielding creation of a useful database and functional maps. Visual representation of MNCH services was well received by project and health planners. This relatively small investment will subsequently enable higher quality of data collection and ease of project planning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".