Evaluation of the child growth monitoring programme in two Zimbabwean provinces
Bibliographic record
Abstract
BACKGROUND: The child growth monitoring (CGM) programme is an important element of nutrition programmes, and when combined with other child health programmes, it can assist in successful management and control of malnutrition in children. AIM: This study aimed to assess the extent to which the CGM programme is able to identify instances of childhood malnutrition and how much this contributes towards malnutrition reduction in Zimbabwe. SETTING: The study was conducted in Manicaland and Matabeleland South provinces of Zimbabwe. The two provinces were purposively selected for having the highest and least proportion of children affected by stunting in the country. METHODS: The CGM programme in Zimbabwe was evaluated using the logic model to assess the ability of the programme to identify growth faltering and link children to appropriate care. RESULTS: Records from 60 health facilities were reviewed. Interviews were conducted with 60 nurses, 100 village health workers (VHWs) and 850 caregivers (300 health facility exit interviews, 450 community based). Nearly all (92%) health facilities visited had functional measuring scales. Twelve health facilities (20%) had no functional height board, with five using warped height boards for measuring children's height. Less than a quarter (21%) of the children had complete records for weight for age and height for age. A large proportion of children eligible for admission for the management of moderate (83%) and severe malnutrition (84%) were missed. CONCLUSION: The CGM programme in Zimbabwe is not well equipped for assessing child height for age and management of children identified with malnutrition, thus failing to timely identify and manage childhood stunting.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".