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Record W4388776917 · doi:10.1177/21582440231208986

Stakeholders’ Perspectives of Enablers and Barriers to Successfully Implementing an Integrated Early Childhood Development Program in an Informal Urban Settlement in Kenya

2023· article· en· W4388776917 on OpenAlexaff
Derrick Ssewanyana, Marie‐Claude Martin, Vibian Angwenyi, Margaret Kabue, Kerrie Proulx, Linlin Zhang, Tina Malti, Eunice Njoroge, Carophine Nasambu, Joyce Marangu, Rachael Odhiambo, Eunice Ombech, Mercy Moraa Mokaya, Emmanuel Kepha Obulemire, Greg Moran, Stephen J. Lye, Kofi Marfo, Amina Abubakar

Bibliographic record

VenueSAGE Open · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsWestern UniversityUniversity of TorontoLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsFocus groupTransparency (behavior)Capacity buildingWorkforceEarly childhoodSettlement (finance)Public relationsInformal settlementsEquity (law)BusinessEconomic growthPsychologyPolitical scienceMarketing

Abstract

fetched live from OpenAlex

Integrated early childhood development (ECD) programs boost child health and developmental outcomes. However, the factors contributing to the successful implementation of such programs in informal urban settlements are not well researched. We conducted 14 focus group discussions and 13 key informant interviews with 125 caregivers of children under the age of 5 years and stakeholders, exploring their views on enablers and barriers to implementing an integrated ECD program in an informal settlement in Kenya. Strategic engagement, capacity building, transparency, fair compensation of ECD workforce, communication skills, and the need to tailor ECD programs to local realities were discussed. An equity-focused implementation approach for integrated ECD programs is timely.

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)0.009
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.004
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
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 head0.034
GPT teacher head0.313
Teacher spread0.278 · 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 designQualitative
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

Citations8
Published2023
Admission routes1
Has abstractyes

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