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Record W4409396305 · doi:10.59247/jahir.v1i3.39

Understanding the Perception of Adolescent Challenges in Body Mass Index Reduction: A Qualitative Study

2024· article· en· W4409396305 on OpenAlexaff
Suwarsi, Joshepine Lorica, Agustina Sri Oktri Hastuti

Bibliographic record

VenueJournal of Advanced Health Informatics Research · 2024
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsBody mass indexPerceptionPsychologyIndex (typography)Reduction (mathematics)Developmental psychologyMedicineComputer scienceMathematicsInternal medicineNeuroscienceWorld Wide Web

Abstract

fetched live from OpenAlex

A weight loss program for adolescents needs to be done immediately, considering that the number of adolescents who are obese is increasing. This research contributes to identifying the challenges faced by overweight adolescents in weight loss programs. Data was collected by filling out a questionnaire given directly to the participants. The location where the research was conducted at Yogyakarta City, Indonesia. The questionnaire has been tested for validity and reliability with a value of more than 0.85. This study uses a qualitative research design, with a sample of adolescents aged 15-19 years who are obese. The number of participants is 15; according to the inclusion criteria, the data is taken through in-depth interviews. Results: The difficulty of overweight adolescents in losing weight is due to the lack of parental support in serving food and the lack of support from friends in diet programs. Health programs for adolescents, especially weight loss programs, need to involve peers and support from parents

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)0.012
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.508
GPT teacher head0.566
Teacher spread0.058 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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