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Record W4386735306 · doi:10.1007/s10943-023-01910-8

When Shepherds Shed: Trajectories of Weight-Related Behaviors in a Holistic Health Intervention Tailored for US Christian Clergy

2023· article· en· W4386735306 on OpenAlexaff
Jia Yao, Dori Steinberg, Elizabeth L. Turner, Grace Cai, Jacqueline R. Cameron, Celia F. Hybels, David E. Eagle, Glen Milstein, Joshua A. Rash, Rae Jean Proeschold‐Bell

Bibliographic record

VenueJournal of Religion and Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsMemorial University of Newfoundland
FundersDuke Global Health Institute, Duke UniversityDuke Endowment
KeywordsWeight lossOverweightIntervention (counseling)Psychological interventionPublic healthHealth behaviorPublic health interventionsObesityBehavior changePsychologyGerontologySleep (system call)Developmental psychologySocial psychologyMedicineEnvironmental healthPsychiatryEndocrinologyNursingComputer science

Abstract

fetched live from OpenAlex

Maintaining healthy behaviors is challenging. Based upon previous reports that in North Carolina (NC), USA, overweight/obese clergy lost weight during a two-year religiously tailored health intervention, we described trajectories of diet, physical activity, and sleep. We investigated whether behavior changes were associated with weight and use of health-promoting theological messages. Improvements were observed in sleep, calorie-dense food intake, and physical activity, with the latter two associated with weight loss. While theological messages were well-retained, their relationship with behaviors depended on the specific message, behavior, and timing. Findings offer insights into weight loss mechanisms, including the role of theological messages in religiously tailored health interventions.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.067
GPT teacher head0.426
Teacher spread0.359 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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