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Record W4205236260 · doi:10.21203/rs.3.rs-15481/v1

Self-reported Precipitating Factors for the Development of Eating Disorders in Young Adulthood: A Preliminary Study

2020· preprint· en· W4205236260 on OpenAlexfundno aff
Cassandra Lenza

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
FundersYork University
KeywordsDietingEating disordersAnxietyPopulationPsychologyPsychiatryClinical psychologyDepression (economics)Young adultDisordered eatingMedicineDevelopmental psychologyEnvironmental healthObesityWeight loss

Abstract

fetched live from OpenAlex

Abstract Objective: This study aimed to identify the perceived precipitating factors for seeking eating disorder treatment in the Millennial population. The purpose of this study was to understand the Millennial population, and determine if self-reported causes of eating concerns are different for this age demographic than that of the general population. Method: An exploratory analysis of the charts of one hundred individuals, mainly women, who sought eating disorder treatment at an outpatient eating disorder treatment center, for the period of 2014-2018, was completed. Respondents met the age criteria and were 18-36 years old. Results: The majority of individuals reported beginning dieting behaviors or restricting their food intake as the main cause of their eating disorder (44%). Discussion: Millennial individuals are comparative to the general public when self-reporting precipitating events that lead to the development of an eating disorder. Risk factors inherent to eating disorders, such as early dieting, body dissatisfaction, anxiety, and depression remain the same for this population.

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)0.019
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0030.004
Research integrity0.0010.007
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 head0.171
GPT teacher head0.479
Teacher spread0.308 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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