MétaCan
Menu
Back to cohort
Record W3012095149 · doi:10.5539/gjhs.v12n4p57

A Behavioral Model for Analysis and Intervention of Healthy Dietary Behavior

2020· article· en· W3012095149 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldPsychology
TopicHuman Behavior and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsMaslow's hierarchy of needsHierarchyBehavior changePsychologyPsychological interventionHabitHealth behaviorIntervention (counseling)Matching (statistics)GerontologySocial psychologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Proper diet is an important way to improve and maintain health, which involves the comprehensive matching of food (category, quantity) and individual (physical fitness, health). Behavior is the key point to achieve this matching. Without behavior change, all cognition and motivations can’t get any tangible health benefits. The aim of this study was to construct a model for analysis and intervention of healthy dietary behavior (HDB). Based on the Integrated Behavioral Model (IBM), Maslow’s hierarchy of needs and Satter’s hierarchy of food needs, this study abstracted the characteristics of longevity, specificity and uncertainty of Healthy Dietary Behavior, constructed a Healthy Dietary Behavioral Model (HDBM), divided 9 negative behaviors of healthy diet (NBHD), and put forward 9 behavior interventions, which could provide ideas for change and habit formation of HDB.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.183
GPT teacher head0.488
Teacher spread0.305 · 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