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Record W2990207431 · doi:10.3386/w21602

Using Behavioral Insights to Increase Parental Engagement: The Parents and Children Together (PACT) Intervention

2015· preprint· en· W2990207431 on OpenAlexaff
Susan Mayer, Ariel Kalil, Philip Oreopoulos, Sebastián Gallegos

Bibliographic record

VenueNational Bureau of Economic Research · 2015
Typepreprint
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of Toronto
FundersUniversity of Chicago
KeywordsPactIntervention (counseling)PsychologyDevelopmental psychologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Parent engagement with their children plays an important role in children's eventual economic success and numerous studies have documented large gaps in parent engagement between low-and higher-income families. While we know remarkably little about what motivates parents to engage in their children's development, recent research suggests that ignoring or discounting the future may inhibit parental investment, while certain behavioral tools may help offset this tendency. This paper reports results from a randomized field experiment designed to increase the time that parents of children in subsidized preschool programs spend reading to their children using an electronic reading application that audio and video records parents as they read. The treatment included three behavioral tools (text reminders, goal-setting, and social rewards) as well as information about the importance of reading to children. The treatment increased usage of the reading application by one standard deviation after the six-week intervention. Our evidence suggests that the large effect size is not accounted for by the information component of the intervention and that the treatment impact was much greater for parents who are more present-oriented than for parents who are less present-oriented.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.538
GPT teacher head0.583
Teacher spread0.046 · 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 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

Citations49
Published2015
Admission routes1
Has abstractyes

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