Economic Evaluation: Costing participatory learning and action cycles with women’s groups to improve feeding, care and dental hygiene for South Asian infants in London
Bibliographic record
Abstract
Abstract Background The Nurture Early for Optimal Nutrition (NEON) programme was designed to promote equitable early childhood development by educating mothers of South Asian origin in east London on optimal feeding, care, and dental hygiene practices. This study conducts a cost analysis of the NEON programme and evaluates its financial sustainability. Methods We conducted an economic costing from the provider perspective and followed a stepdown procedure to identify all costs incurred from December 2019, the initiation of the trial, to May 2023, the completion of final evaluation and dissemination. Costs associated with start-up, implementation, and monitoring and evaluation activities are differentiated. Affordability analysis was conducted with respect to the budget of the local authorities. Results The total cost of NEON design and delivery in Newham and Towe Hamlets was £75,992 ($INT 114,445), with 45% for staff salaries, 50% for material, and 5% for capital investment. The start-up stage cost 57% while the implementation stage cost 43%. The average cost per mother participating in the programme was £409($INT 615). The total cost of trial delivery in Newham accounted for around 0.053% of the borough’s annual child development expenditure, while the total trial cost in Tower Hamlets was equivalent to 0.003% of its’ spending on children’s development. Conclusion The delivery of NEON is largely within local authorities’ budget for childhood development. The unit cost is expected to decrease when sharing costs are spread across more participants and implementing systems are validated and well developed.
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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 | 0.076 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".