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Record W2600425448 · doi:10.1177/0192513x17698183

Predicting Mother–Adolescent Sexual Communication Using the Integrative Model of Behavioral Prediction

2017· article· en· W2600425448 on OpenAlexafffund
E. Sandra Byers, Heather A. Sears, Kathleen Hughes

Bibliographic record

VenueJournal of Family Issues · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyDevelopmental psychologyPath analysis (statistics)Sexual behaviorPerceptionReproductive healthClinical psychologyPopulationMedicine

Abstract

fetched live from OpenAlex

Guided by the integrative model of behavioral prediction, we examined factors associated with more extensive mother–adolescent sexual communication. Participants were 303 mothers of young adolescents who completed a survey assessing their skills, attitudes about the outcomes of sexual health discussions, perceptions of social norms, self-efficacy, and sexual communication intentions at baseline (Time 1) and the extent of their sexual communication with their adolescent 6 months later (Time 2). A path analysis showed that, after accounting for the correlations among the four predictors and sexual communication at Time 1, intentions mediated the relationships between mothers’ skills, attitudes, and self-efficacy and the extent of their sexual communication with their adolescent. Attitudes also were related directly to mothers’ sexual communication. These results provide support for the utility of the integrative model of behavioral prediction for identifying parent characteristics that are related to more extensive parent–adolescent sexual communication and one mechanism by which these relationships may occur.

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)0.005
metaresearch head score (Gemma)0.020
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 score0.010
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.369
GPT teacher head0.526
Teacher spread0.158 · 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

Citations15
Published2017
Admission routes2
Has abstractyes

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