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Record W4414240312 · doi:10.3390/children12091241

Do Playful Parenting Programs Implemented at Scale Improve Caregiver Practices and Child Development?

2025· article· en· W4414240312 on OpenAlexaff
Carina Omoeva, Rafael Contreras Gomez, Rachel Hatch, Frances E. Aboud, Ania Chaluda, Given Hapunda, Karma Choden, Francis Sichimba, Ksenija Krstić, Jill Popp

Bibliographic record

VenueChildren · 2025
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill University
FundersLEGO FoundationUNICEF
KeywordsFocus groupScale (ratio)Program evaluationQuality (philosophy)Child developmentParenting skillsPositive parentingChild care

Abstract

fetched live from OpenAlex

Background/Objectives: As an independent research group, we examined parent and child outcomes of three different parenting programs delivered at scale. The programs were implemented in Bhutan, Serbia and Zambia by different organizations. Methods: Mixed methods included a caregiver interview using the HOME Inventory, a direct child assessment using the Global Scales of Early Development (GSED) and focus group discussions with caregivers (FGD). Sampled mothers and children were randomly selected for the HOME/GSED: Bhutan n = 432, Serbia n = 636, Zambia n = 1024. Over 40 mothers and fathers of children under 3 years were purposively selected for FGD. Intention-to-treat and secondary regression analyses of attendees and non-attendees were conducted on the HOME and GSED; FGDs were subject to content analysis. Results: Parenting practices were found to be minimally (Bhutan) or modestly (Zambia) higher for caregivers who attended group sessions. Caregivers in Serbia who recalled receiving play messages had higher HOME scores. Child outcomes showed small (Bhutan) or no differences (Serbia, Zambia) associated with participation. Conclusions: Explanations focused on limits to program participation in scaled programs, the need for pilot evaluations to ensure that the program design is effective, and the need to monitor delivery quality and other implementation processes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.290
Teacher spread0.277 · 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 teacher head, 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

Citations2
Published2025
Admission routes1
Has abstractyes

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