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Record W4298362281 · doi:10.1080/15295192.2022.2087040

The Future of Parenting Programs: I Design

2022· article· en· W4298362281 on OpenAlexfundno aff
Marc H. Bornstein, Lucie Cluver, Kirby Deater‐Deckard, Nancy E. Hill, Justin Jager, Sonya Krutikova, Richard M. Lerner, Hirokazu Yoshikawa

Bibliographic record

VenueParenting · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
FundersNational Institute of Child Health and Human DevelopmentEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentEconomic and Social Research CouncilNational Institutes of HealthTempleton World Charity FoundationGlobal Challenges Research FundNew York University Abu DhabiUniversity of OxfordHarvard UniversityArizona State UniversityUniversity of Cape TownUK Research and InnovationYork UniversityUNICEF
KeywordsProgram Design LanguageControl (management)Research designComputer scienceDesign elements and principlesSelection (genetic algorithm)PopulationProgram evaluationPsychologyManagement scienceEngineeringMedicineSoftware engineeringPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

SYNOPSISParenting programs worldwide (and especially in low- and middle-income countries) support parents in their caregiving roles. Parenting programs are popular and prolific, but many outright fail to deliver meaningful effects or eventuate in only small effects. Incomplete consideration and execution of many design features of programs can account for these shortfalls. This article delimits several critical criteria surrounding successful design and evaluation of evidence-based parenting programs. Specific factors include important preliminary questions concerning details of program design, such as whether the topic of the parenting program specifies the aspect(s) of parenting to be encouraged or discouraged and what theory of change underlies the program; program design contents concern subject matter development, sources, and messages; program design components specify the delivery mode, effectiveness, location, and alignment; program design targeting and sampling concern whom the program is addressing, why, and whether the program is designed to be universal or targeted to a specific population; ensuring reliable and valid program measurement; and rigorous experimental standards that encompass evaluating program effectiveness, including randomized control trial or quasi-experimental designs and the selection of control and comparison conditions. Policy makers, program leaders, investigators, and, of course, parents and children all benefit when parenting programs are well designed.Objective.Design.Results.Conclusions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.036
GPT teacher head0.303
Teacher spread0.267 · 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.

Study designNot applicable
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

Citations32
Published2022
Admission routes1
Has abstractyes

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