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Record W3094931694 · doi:10.3389/fpubh.2020.567900

A Multi-Sectoral Approach Improves Early Child Development in a Disadvantaged Community in Peru: Role of Community Gardens, Nutrition Workshops and Enhanced Caregiver-Child Interaction: Project “Wawa Illari”

2020· article· en· W3094931694 on OpenAlexafffund
Doris González‐Fernández, Ana Sofía Mazzini Salom, Fermina Herrera Bendezu, Sonia Huamán, Bertha Rojas Hernández, Illène Pevec, Eliana Mariana Galarza Izquierdo, Nicoletta Armstrong, Virginia Thomas, Sonia Vela Gonzáles, Carlos Gonzáles Saravia, Marilyn E. Scott, Kristine G. Koski

Bibliographic record

VenueFrontiers in Public Health · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill University
FundersInstitut National Du CancerGrand Challenges CanadaMcGill University
KeywordsPsychological interventionEnvironmental healthGross motor skillMedicineDisadvantagedChild developmentAnthropometryGerontologyFood securityPediatricsPsychologyMotor skillNursingPsychiatryGeography

Abstract

fetched live from OpenAlex

Background: Multi-dimensional monitoring evaluation and learning strategies are needed to address the complex set of factors that affect early child development in marginalized populations, but few studies have explored their effectiveness. Objective: To compare improvement of health and development of children 0-3 years between intervention communities (IC) and control communities (CC) from peripheral settlements of Lima. Sequential interventions included: 1) home and community gardens, 2) conscious nutrition, and 3) parenting workshops following the International Child Development Program (ICDP). Methods: Interventions were delivered by community health promoters (CHPs) using a ‘step-by-step’ learning system. Both IC and CC were monitored before the interventions began, at 8 and 12 months (n = 113 IC and 127 CC children). Data were collected on household characteristics, diet, food security, health indicators (history of diarrhea and respiratory infections, hemoglobin, intestinal parasites, anthropometry), caregiver-child interactions and stress, and achievement of Pan-American Health Organization age-specific developmental milestones. Stepwise multiple logistic regressions were used to determine if the interventions affected food insecurity, as well as motor, social/cognitive and language delays. Results: At baseline, 2.6% were categorized as ‘suspected developmental delay’ and 14.2% were on ‘alert for development delay’. Food insecurity, diarrhea and respiratory infections were lowered following the interventions. Through the ‘step-by-step’ approach, caregivers in IC gained skills in gardening, conscious nutrition and parenting that reduced the risk of food insecurity [Adjusted Risk Ratio= 0.20 (95% CI: 0.08-0.51)] and language delay [0.39 (0.19-0.82)] but not motor or social/cognitive delay. Use of a multiple micronutrient supplement decreased the risk of motor delay [0.12 (0.03-0.56)], but more pets were associated with higher risk of motor [3.24 (1.47-7.14)] and social/cognitive delay [2.72 (1.33-5.55)], and of food insecurity [1.73 (1.13-2.66)]. Conclusion: The combined interventions delivered by CHPs helped to mitigate the impact of adversity on food insecurity and language delay. Additional improvements may have been detected if the interventions had continued for a longer time. Our results indicate that control of infections and pets may be needed to achieve measurable results for motor and social/cognitive development. Continuous monitoring facilitated adjusting implementation strategies and achieving positive developmental outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.043
GPT teacher head0.299
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes2
Has abstractyes

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