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Record W4387909966 · doi:10.3138/cjhs.2022-0052

Influences on sexting in an intimate relationship: Motivations, risks, communication, personality traits, and relationship variables

2023· article· en· W4387909966 on OpenAlexaffvenue
Tasha Falconer, Terry P. Humphreys, Fergal O’Hagan, Jessica Johnson

Bibliographic record

VenueThe Canadian Journal of Human Sexuality · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsTrent UniversityUniversity of Guelph
Fundersnot available
KeywordsPsychologyCasualSocial psychologyGratificationBig Five personality traitsSensation seekingPersonalityDevelopmental psychology

Abstract

fetched live from OpenAlex

People in intimate relationships sext more than those in casual relationships or those who are single. Past research indicates a differential pattern in sexting behaviour based on relationship status, so a closer look at how sexting might serve the sexual and relationship needs of those in intimate relationships is warranted. In this study, we used quantitative and qualitative methods to investigate several factors that influence sexting behaviour within intimate relationships. Participants ( N = 771; 76% women, 23% men, 0.5% non-binary) completed an online questionnaire about personality traits, satisfaction, communication, trust, commitment, motivations, and risks that included open-text space to elaborate responses. This study used incentive motivational theory as a guide. Results suggest that for those in committed relationships, sexting is a way to have intimate connection that is mediated by technology. Participants were motivated to sext for several reasons, but most commonly for sexual gratification. Participants did not perceive there to be any risks to sexting with their partner. Sexting was found to be positively related to relationship and sexual satisfaction, commitment, erotophilia, and sexual sensation seeking. The therapeutic implications of these results are discussed.

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.218
GPT teacher head0.405
Teacher spread0.187 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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