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Record W2562065619 · doi:10.14740/jocmr2868w

Hispanic Patient Perspectives of the Physician’s Role in Obesity Management

2016· article· en· W2562065619 on OpenAlexvenueno aff
Colton Ragsdale, Justin Wright, Gurjeet S Shokar, Rebekah Salaiz, Navkiran K. Shokar

Bibliographic record

VenueJournal of Clinical Medicine Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
Fundersnot available
KeywordsWeight lossMedicineWeight managementObesityFamily medicineManagement of obesityHealth carePerceptionCoding (social sciences)NursingPsychologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known concerning Hispanic patients' perceptions about the role of the physician in obesity management. This study seeks to describe the perspectives of Hispanic patients toward weight loss, and what they believe their doctor's role should be in the management of obesity. METHODS: A cross-sectional study utilizing semi-structured interviews was conducted in a university-based family medicine clinic. Open-ended questions explored beliefs about the relationship between weight and health, previous weight loss experience, perceptions about the role of the physician in weight loss, past experiences with their physician, and preferences for how a physician could help facilitate weight loss. The free recall listing technique was used to elicit responses. Common themes were identified by a group coding process. RESULTS: Patients were open to discussion from physicians concerning weight loss but many had not been approached. They wanted assistance from their doctors in the form of dietician referrals, specific weight loss goals, and encouragement. Patients' knowledge about the implications of excess weight on health was lacking. CONCLUSION: Hispanic patients want more help and advice from their doctors. General knowledge of the health implications of obesity was lacking, indicating a need for more health education by the healthcare team.

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)0.022
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, 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 score0.181
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.289
GPT teacher head0.636
Teacher spread0.346 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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