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Record W2791423700 · doi:10.1123/apaq.2017-0193

Conceptualizing Obesity as a Chronic Disease: An Interview With Dr. Arya Sharma

2018· article· en· W2791423700 on OpenAlexaffabout
Arya M. Sharma, Donna L. Goodwin, Janice Causgrove Dunn

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2018
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsObesityCoping (psychology)GerontologyPsychological interventionMoodPhysical activityDiseasePsychologyMedicinePsychiatryPhysical therapyEndocrinology

Abstract

fetched live from OpenAlex

Dr. Arya M. Sharma challenges the conventional wisdom of relying simply on "lifestyle" approaches involving exercise, diet, and behavioral interventions for managing obesity, suggesting that people living with obesity should receive comprehensive medical interventions similar to the approach taken for other chronic diseases such as Type 2 diabetes or hypertension. He purports that the stigma-inducing focus on self-failing (e.g., coping through food, laziness, lack of self-regulation) does not address biological processes that make obesity a lifelong problem for which there is no easy solution. Interdisciplinary approaches to obesity are advocated, including that of adapted physical activity. Physical activity has multifaceted impacts beyond increasing caloric expenditure, including improved sleep, better mood, increased energy levels, enhanced self-esteem, reduced stress, and an enhanced sense of well-being. The interview with Dr. Sharma, transcribed from a keynote address delivered at the North American Adapted Physical Activity Symposium on September 22, 2016, in Edmonton, AB, Canada, outlines his rationale for approaching obesity as a chronic disease.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.005

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 head0.094
GPT teacher head0.449
Teacher spread0.355 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations7
Published2018
Admission routes2
Has abstractyes

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