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Record W4241662963 · doi:10.32920/ryerson.14655402.v1

The effect of a "real-world" media intervention on body image

2021· preprint· en· W4241662963 on OpenAlexaff
Stephanie L Quigg

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsSystems, Applications & Products in Data Processing (Canada)
Fundersnot available
KeywordsMoodIntervention (counseling)PsychologyControl (management)Applied psychologyClinical psychologySocial psychologyAdvertisingPsychiatryComputer science

Abstract

fetched live from OpenAlex

The present research investigates whether a brief "real world" media intervention -- a short television advertisement -- can be an effective mitigator of the adverse effects of media portrayals of women on viewers' body satisfaction. Two hundred female university students (age 17 to 29, M = 19.54) were randomly assigned to one of two television conditions (music video or control), one to two commercial conditions ("intervention" or control), and completed self-report measures of television behavior, media influence, body satisfaction and mood. Participants who viewed music videos did not report significantly different levels of body dissatisfaction than participants who viewed control television, although results trended in the hypothesized direction. A significant interaction between program and commercial emerged assessing mood as the outcome variable. Participants who viewed the music videos and were not exposed to the intervention commercial reported significantly lower mood than participants in the other three conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0070.000

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 head0.016
GPT teacher head0.363
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations0
Published2021
Admission routes1
Has abstractyes

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