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Record W4298087408 · doi:10.2196/37449

Measuring and Enhancing Initial Parent Engagement in Parenting Education: Experiment and Psychometric Analysis

2022· article· en· W4298087408 on OpenAlexvenueno aff
Isaac A. Mirzadegan, Amelia C Blanton, Alexandria Meyer

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2022
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCompetence (human resources)Parenting skillsDevelopmental psychologyParent trainingPsychological interventionExploratory factor analysisClinical psychologyIntervention (counseling)PsychometricsSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Prevention efforts focused on parenting can prevent and reduce the rates of child internalizing and externalizing problems, and positive changes in parenting skills have been shown to mediate improvements in child behavioral problems. However, parent skills training programs remain underused, with estimates that under half of eligible parents complete treatment and even lower rates engage in preventive interventions. Moreover, there is no validated measure to assess initial engagement in parent education or skills training, which is an understudied stage of parent engagement. OBJECTIVE: We aimed to test a novel engagement strategy, exploring whether including information pertaining to the neuroscience of child development and parent skills training enhanced parental intent to enroll. In addition, a novel self-report measure, the 18-item Parenting Resources Acceptability Measure (PRAM), was developed and validated. METHODS: In a group of 166 parents of children aged 5 to 12 years, using an engagement strategy based on the Seductive Allure of Neuroscience Explanations, we conducted a web-based experiment to assess whether the inclusion of neuroscience information related to higher levels of engagement via self-report and behavioral measures. The PRAM was subjected to an exploratory factor analysis and examined against relevant validity measures and acceptability measurement criteria. RESULTS: Three PRAM factors emerged ("Acceptability of Parenting Resources," "Interest in Learning Parenting Strategies," and "Acceptability of Parenting Websites"), which explained 68.4% of the total variance. Internal consistency among the factors and the total score ranged from good to excellent. The PRAM was correlated with other relevant measures (Parental Locus of Control, Parenting Sense of Competence, Strengths and Difficulties Questionnaire, Parent Engagement in Evidence-Based Services, and behavioral outcomes) and demonstrated good criterion validity and responsiveness. Regarding the engagement manipulation, parents who did not receive the neuroscience explanation self-reported lower interest in learning new parenting skills after watching an informational video compared with parents who did receive a neuroscience explanation. However, there were no significant differences between conditions in behavioral measures of intent to enroll, including the number of mouse clicks, amount of time spent on a page of parenting resources, and requests to receive parenting resources. The effects did not persist at the 1-month follow-up, suggesting that the effects on engagement may be time-limited. CONCLUSIONS: The findings provide preliminary evidence for the utility of theory-driven strategies to enhance initial parental engagement in parent skills training, specifically parental interest in learning new parenting skills. In addition, the study findings demonstrate the good initial psychometric properties of the PRAM, a tool to assess parental intent to enroll, which is an early stage of engagement.

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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)0.014
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.060
GPT teacher head0.336
Teacher spread0.276 · 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 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
Published2022
Admission routes1
Has abstractyes

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