The influence of the Community‐based Health Planning and Services (CHPS) program on community health sustainability in the Upper West Region of Ghana
Bibliographic record
Abstract
Ghana introduced Community-based Health Planning and Services (CHPS) to improve primary health care in rural areas. The extension of health care services to rural areas has the potential to increase sustainability of community health. Drawing on the capitals framework, this study aims to understand the contribution of CHPS to the sustainability of community health in the Upper West Region of Ghana-the poorest region in the country. We conducted in-depth interviews with community members (n = 25), key informant interviews with health officials (n = 8), and focus group discussions (n = 12: made up of six to eight participants per group) in six communities from two districts. Findings show that through their mandate of primary health care provision, CHPS contributed directly to improvement in community health (eg, access to family planning services) and indirectly through strengthening social, human, and economic capital and thereby improving social cohesion, awareness of health care needs, and willingness to take action at the community level. Despite the current contributions of CHPS in improving the sustainability of community health, there are several challenges, based on which we recommend, that government should increase staffing and infrastructure in order to strengthen and maintain the functionality of CHPS.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".