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Record W2161430803 · doi:10.1111/cch.12301

No data, no problem, no action: parenting programs in low‐income countries. Making the social–emotional outcomes more visible

2015· article· en· W2161430803 on OpenAlexaff
A. Tinajero, Nancy J. Cohen, S. Ametorwo

Bibliographic record

VenueChild Care Health and Development · 2015
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of TorontoBaycrest Hospital
Fundersnot available
KeywordsAction (physics)PsychologyLow incomeDevelopmental psychologySocial emotional learningDemographic economicsEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Parenting programs are delivered in many low income countries in an effort to address the effects of insufficient cognitive stimulation, major social-emotional risk factors related to inadequate caregiver sensitivity and responsiveness, maternal depression, violence and biological risk factors. However, the outcomes of these programs are often untested because of methodological limitations in the existing evaluation tools available for cross-cultural research, and especially those related to social-emotional development and functioning. METHOD: This study takes an approach that involves organizing data from focus groups collected following caregiver participation in a parenting program, Learning Through Play (LTP) delivered in 11 low income countries. The LTP program has as its aim to increase parent knowledge and influence parental practice regarding the essential components of early development of children aged birth to 6 years. The purpose of systematically organizing focus group data was to illustrate a means by which program developers worldwide can discuss outcomes of program participation, particularly in social-emotional domains that have been overlooked in past studies on parenting programs. RESULTS AND CONCLUSION: The findings from this study suggest that qualitative outcome data can be organized to make the social-emotional outcomes of participation in parenting programs more visible.

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)0.001
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 score0.422
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.085
GPT teacher head0.368
Teacher spread0.283 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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