Contextual variations in costs for a community health strategy implemented in rural, peri-urban and nomadic sites in Kenya
Bibliographic record
Abstract
BACKGROUND: Many low and middle income countries have developed community health strategies involving lay health workers, to complement and strengthen public health services. This study explores variations in costing parameters pertinent to deployment of community health volunteers across different contexts outlining considerations for costing program scale-up. METHODS: The study used quasi experimental study design and employed both quantitative and qualitative methods to explore community health unit implementation activities and costs and compare costs across purposively selected sites that differed socially, economically and ecologically. Data were collected from November 2010 to December 2013 through key informant interviews and focus group discussions. We interviewed 16 key informants (eight District community health strategy focal persons, eight frontline field officers), and eight focus group discussions (four with community health volunteers and four with community health committee) and 560 sets of monthly cost data. Cost data were tabulated using Microsoft Excel. Qualitative data were transcribed and coded using a content analysis framework. RESULTS: Four critical elements: attrition rates for community health volunteers, geography and population density, livelihood opportunity costs and benefits, and social opportunity benefits, drove cost variations across the three sites. Attrition rate was highest in peri-urban site where population is highly mobile and lowest in nomadic site. More households were covered by community health workers in the peri-urban area making per capita costs considerably less than in the nomadic settings where long distances had to be covered to reach sparsely distributed households. Livelihood opportunity costs for Community Health Volunteers were highest in nomadic setting, while peri-urban ones reported substantial employability benefits resulting from training. Social opportunity benefits were highest in rural site. CONCLUSIONS: Results show that costs of implementing community health strategy varied due to different area contextual factors in Kenya. This study identified four critical elements that drive cost variations: attrition rates for community health volunteers, geography and population density, livelihood opportunity costs and benefits, and social opportunity benefits. Health programme managers and policy-makers need to pay attention to details of contextual factors in costing for effective implementation of community health strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".