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Record W3174071573 · doi:10.1177/10731911211025628

Psychometric Properties of the Coercion in Intimate Partner Relationships Scale

2021· article· en· W3174071573 on OpenAlexaff
Kathleen Wilson, Patti A. Timmons Fritz

Bibliographic record

VenueAssessment · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPsychologyDiscriminant validityCoercion (linguistics)Reliability (semiconductor)Confirmatory factor analysisScale (ratio)Construct validityClinical psychologyConcurrent validitySample (material)Construct (python library)Internal consistencySocial psychologyPsychometricsStructural equation modelingComputer science

Abstract

fetched live from OpenAlex

Coercive control is defined as the systematic use of demands, threats, and surveillance behaviors to gain control over an individual. Content validity appears to be an issue for existing measures of coercive control tactics, as they do not assess all of these behaviors. This study investigated the validity and reliability of the Demand, Threat, Surveillance, and Response to Demands subscales of the Coercion in Intimate Partner Relationships (CIPR) scale. Participants ( N = 541) completed online measures of coercive control, physical intimate partner violence, depression, and posttraumatic stress disorder symptomatology. Confirmatory factor analyses, linear regressions, and correlational analyses investigated the construct (i.e., concurrent, convergent, and discriminant) validity of the CIPR subscales. Internal consistency of the subscales and test–retest reliability were also examined. Results provided support for the validity and reliability of the CIPR. Implications and usage of the CIPR in research and practice are discussed. We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study.

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)0.001
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 score0.208
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.083
GPT teacher head0.369
Teacher spread0.286 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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