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Conflict resolution patterns and violence perpetration in adolescent couples: A gender‐sensitive mixed‐methods approach

2016· article· en· W2299373104 on OpenAlexafffund
Mylène Fernet, Martine Hébert, Alison Paradis

Bibliographic record

VenueJournal of Adolescence · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsPsychologyConflict resolutionNegotiationPerspective (graphical)Social psychologyConflict resolution strategyResolution (logic)Developmental psychologyAffect (linguistics)Sample (material)CommunicationSociology

Abstract

fetched live from OpenAlex

This study used a sequential two-phase explanatory design. The first phase of this mixed-methods design aimed to explore conflict resolution strategies in adolescent dating couples, and the second phase to document, from both the perspective of the individual and of the couple, dyadic interaction patterns distinguishing youth inflicting dating violence from those who do not. A sample of 39 heterosexual couples (mean age 17.8 years) participated in semi-structured interviews and were observed during a 45 min dyadic interaction. At phase 1, qualitative analysis revealed three main types of conflict resolution strategies: 1) negotiating expectations and individual needs; 2) avoiding conflicts or their resolution; 3) imposing personal needs and rules through the use of violence. At phase 2, we focused on couples with conflictive patterns. Results indicate that couples who inflict violence differ from nonviolent couples by their tendency to experience conflicts when in disagreement and to resort to negative affects as a resolution strategy. In addition, while at an individual level, they show a tendency to withdraw from conflict and to use less positive affect, at a dyadic level they present less symmetry. Results offer important insights for prevention programs.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.432
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.059
GPT teacher head0.366
Teacher spread0.307 · 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.

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

Citations31
Published2016
Admission routes2
Has abstractyes

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