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Record W2964803643 · doi:10.1186/s12889-019-7375-z

Measuring 8 to 12 year old children’s self-report of power imbalance in relation to bullying: development of the Scale of Perceived Power Imbalance

2019· article· en· W2964803643 on OpenAlexaff
Helen Nelson, Garth Kendall, Sharyn Burns, Kimberly A. Schonert‐Reichl, Robert Kane

Bibliographic record

VenueBMC Public Health · 2019
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsLearning PartnershipUniversity of British Columbia
FundersAustralian Government
KeywordsBiostatisticsConfirmatory factor analysisStructural equation modelingExploratory factor analysisPsychological interventionConstruct validityScale (ratio)Applied psychologyConstruct (python library)MedicinePower (physics)PsychometricsPublic healthSocial psychologyPsychologyClinical psychologyNursingStatisticsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: While power imbalance is now recognized as a key component of bullying, reliable and valid measurement instruments have yet to be developed. This research aimed to develop a self-report instrument that measures power imbalance as perceived by the victim of frequent aggressive behavior. METHODS: A mixed methods approach was used (468 participants, Grade 4 to 6). This paper describes the exploratory (n = 111) and confirmatory factor analysis of the new instrument (n = 337), and assessment of reliablity and construct validity. RESULTS: A 2-factor model represented physical and social aspects of power imbalance (n = 127: normed chi-square = 1.2, RMSEA = .04, CF1 = .993). The social factor included constructs of group and peer valued characteristics. CONCLUSIONS: This research will enhance health and education professionals understanding of power imbalance in bullying and will inform the design and evaluation of interventions to address bullying in children.

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)0.002
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 score0.018
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.026
GPT teacher head0.285
Teacher spread0.259 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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