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Record W4384406654 · doi:10.3389/fpsyg.2023.1168423

Sexual harassment and abuse; disclosure and awareness of report- and support resources in Norwegian sport- and non-sport high schools: a prospective exploratory study

2023· article· en· W4384406654 on OpenAlexaff
Nina Sølvberg, Monica Klungland Torstveit, Margo Mountjoy, Jan H. Rosenvinge, Gunn Pettersen, Jorunn Sundgot‐Borgen

Bibliographic record

VenueFrontiers in Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNorwegianPsychologyHarassmentTest (biology)AthletesRecreationClinical psychologyApplied psychologyMedical educationSocial psychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex

Purpose: To examine high school students' disclosure of sexual harassment and abuse (SHA), and awareness of reporting systems and support mechanisms in school among students, leaders, and coaches. Method: = 249) at the participating high schools responded to an adapted version of the questionnaire at T1. Data were analyzed using ANOVA or Welch test, Pearson Chi-Square test, and McNemar test. Results: In total, 11.4 and 34.0% of the adolescents were aware of reporting systems and support mechanisms, respectively, in their schools. Nearly all the leaders, and half of the coaches were aware of these resources. Among the adolescents with lifetime experience of SHA, 20.1% had disclosed their experiences to someone. Girls disclosed more frequently than boys. The elite- and recreational athletes disclosed less often compared with the reference students. A negative change from T1 to T2 was found in disclosure of SHA and awareness of support mechanisms. At T2, 6.5% of the adolescents reported that their school had implemented measures against SHA during the last 12 months. Conclusion: The results emphasize a need for institutional effort to improve information about available report- and support resources and increase the relevance of use of such systems for adolescents.

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 score0.125
Threshold uncertainty score0.628

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 studies0.0000.001
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 head0.351
Teacher spread0.325 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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