Reliability of length measurements collected by community nurses and health volunteers in rural growth monitoring and promotion services
Bibliographic record
Abstract
BACKGROUND: Length measurements are important in growth, monitoring and promotion (GMP) for the surveillance of a child's weight-for-length and length-for-age. These two indices provide an indication of a child's risk of becoming wasted or stunted, and are more informative about a child's growth than the widely used weight-for-age index (underweight). Although the introduction of length measurements in GMP is recommended by the World Health Organization, concerns about the reliability of length measurements collected in rural outreach settings have been expressed by stakeholders. Our aim was to describe the reliability and challenges associated with community health personnel measuring length for rural outreach GMP activities. METHODS: Two reliability studies (A and B), using 10 children less than 24 months each, were conducted in the GMP services of a rural district in Ghana. Fifteen nurses and 15 health volunteers (HV) with no prior experience in length measurements were trained. Intra- and inter-observer technical error of measurement (TEM), average bias from expert anthropometrist, and coefficient of reliability (R) of length measurements were assessed and compared across sessions. Observations and interviews were used to understand the ability and experiences of health personnel with measuring length at outreach GMP. RESULTS: Inter-observer TEM was larger than intra-observer TEM for both nurses and HV at both sessions and was unacceptably (compared to error standards) high in both groups at both time points. Average biases from expert's measurements were within acceptable limits, however, both groups tended to underestimate length measurements. The R for lengths collected by nurses (92.3%) was higher at session B compared to that of HV (87.5%). Length measurements taken by nurses and HV, and those taken by an experienced anthropometrist at GMP sessions were of moderate agreement (kappa = 0.53, p < 0.0001). CONCLUSIONS: The reliability of length measurements improved after two refresher trainings for nurses but not for HV. In addition, length measurements taken during GMP sessions may be susceptible to errors due to overburdened health personnel and crowded GMP clinics. There is need for both pre- and in-service training of nurses and HV on length measurements and procedures to improve reliability of length measurements.
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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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".