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Record W2587438732 · doi:10.2196/jmir.5636

Association Between Internet Use and Body Dissatisfaction Among Young Females: Cross-Sectional Analysis of the Canadian Community Health Survey

2017· article· en· W2587438732 on OpenAlexaffabout
Allison Carter, Jamie I. Forrest, Angela Kaida

Bibliographic record

VenueJournal of Medical Internet Research · 2017
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of British ColumbiaAIDS VancouverSimon Fraser University
Fundersnot available
KeywordsCross-sectional studyThe InternetAssociation (psychology)PsychologyEnvironmental healthMedicineDemographySociologyWorld Wide Web

Abstract

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BACKGROUND: Recent research suggests Internet exposure, including Facebook use, is positively correlated with body dissatisfaction, especially among girls and young women. Canada has one of the highest Internet access rates in the world, yet no previous study has examined this relationship using nationally representative data. OBJECTIVE: Our objective was to evaluate the relationship between Internet use and body dissatisfaction among a national, population-based sample of Canadian females 12-29 years of age. METHODS: We used cross-sectional data from the Canadian Community Health Survey 2011-2012. Body dissatisfaction was measured using a 5-point Likert scale and defined as "very dissatisfied/dissatisfied" with one's body. The explanatory variable was time spent using the Internet per week in the past 3 months, ranging from none/<1 hour to >20 hours. We used multinomial logistic regression to investigate whether greater Internet use was associated with increasing odds of being very dissatisfied/dissatisfied, neutral, or satisfied with one's body, using very satisfied as the referent. Probability survey sampling weights were applied to all analyses. RESULTS: Of 2983 included participants, sampled to represent 940,786 young Canadian females, most were 20-29 years old (61.98%) and living in households with an annual income Can $80,000 or more (44.61%). The prevalence of body dissatisfaction was 14.70%, and 25- to 29-year-olds were more likely than 12- to 14-year-olds to be very dissatisfied or dissatisfied with their body (20.76% vs 6.34%). Few (5.01%) reported none/<1 hour of Internet use, over half (56.93%) reported 1-10 hours, and one-fifth (19.52%) reported spending >20 hours online per week. Adjusting for age and income, the odds of being very dissatisfied/dissatisfied, relative to very satisfied, were greater in the highest versus lowest Internet use group (adjusted odds ratio [AOR] 3.03, 95% CI 1.19-7.70). The AORs for this level of body dissatisfaction increased across increasing levels of Internet use, ranging from 0.88 (95% CI 0.35-2.21) to 3.03 (95% CI 1.19-7.70). Additionally, those who spent 11-14 hours online were more likely to be neutral (AOR 3.66, 95% CI 1.17-11.45) and those who spent 15-20 hours online were more likely to be neutral (AOR 4.36, 95% CI 1.18-16.13) or satisfied (AOR 2.82, 95% CI 1.14-7.01) with their bodies, relative to very satisfied, compared with those spending no time or <1 hour online. CONCLUSIONS: A substantial proportion of Canadian females 12-29 years of age spent large amounts of time (>20 hours) on the Internet each week, and body dissatisfaction was significantly more likely among this group. Those who spent 11-20 hours online were also more likely to be less satisfied with their bodies. Efforts are needed to support girls and young women to achieve and maintain a positive body image in today's digital age.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.240
GPT teacher head0.517
Teacher spread0.276 · 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 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

Citations28
Published2017
Admission routes2
Has abstractyes

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