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Record W4200042846 · doi:10.1111/cch.12940

‘Teenagers are into perfect‐looking things’: Dating, sexual attitudes and experiences of adolescents with severe obesity

2021· article· en· W4200042846 on OpenAlexafffund
Órla Walsh, Elizabeth Dettmer, Andrea Regina, Stella Dentakos, Jennifer Christian, Jill Hamilton, Alène Toulany

Bibliographic record

VenueChild Care Health and Development · 2021
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick Children
KeywordsThematic analysisPsychosocialReproductive healthObesityBody mass indexPsychologyMedicineDevelopmental psychologyQualitative researchClinical psychologyPsychiatryPopulationEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: This qualitative study explored the dating and sexual health attitudes and behaviours among adolescents with severe obesity (body mass index [BMI] > 99th%) attending a multidisciplinary weight-management programme. METHODS: = 16.8) and analysed through reflective thematic analysis. RESULTS: Participants described polarized dating behaviours in which dating and sexual relationships were either avoided due to this not being a priority, lack of time, feared rejection, and/or body size as perceived barrier or in contrast, when approached, involved greater sexual risk. CONCLUSIONS: These findings have numerous implications including the need for increased education on the romantic developmental challenges faced by adolescents with severe obesity, the importance of ongoing screening of high-risk sexual behaviours and body dissatisfaction from frontline care providers, and the ability to support referrals to psychosocial services when appropriate.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.004
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.014
GPT teacher head0.296
Teacher spread0.283 · 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 designQualitative
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

Citations5
Published2021
Admission routes2
Has abstractyes

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