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Record W2512135938 · doi:10.1037/hea0000409

Implicit processes in health psychology: Diversity and promise.

2016· article· en· W2512135938 on OpenAlexaff
Paschal Sheeran, Jos A. Bosch, Geert Crombez, Peter A. Hall, Jennifer L. Harris, Esther K. Papies, Reínout W. Wiers

Bibliographic record

VenueHealth Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Waterloo
FundersJohn Templeton Foundation
KeywordsPsycINFOPsychologyDiversity (politics)CognitionCognitive psychologyBehavior changeHealth psychologyPublic healthSocial psychologyApplied psychologyMEDLINEMedicineSociology

Abstract

fetched live from OpenAlex

Implicit processes refer to cognitive, affective, and motivational processes that influence health decisions and behavior without the person intending that influence. This special issue aims to increase appreciation of the diverse and promising research on implicit processes in health psychology, and to promote discussion about how this research improves understanding of health behavior change and can be harnessed to meet public health mandates. The articles included in the special issue showcase this diversity and promise, and present not only new findings, but also new theories, new measures, and state-of-the- art summaries of progress. The research demonstrates the added value of considering implicit processes for understanding health behaviors, their interactions with explicit processes and neural mechanisms, as well as the benefits of targeting implicit processes in health behavior interventions. At the same time, however, the papers in this special issue also point to potential boundary conditions, the importance of good measures and appropriate tests of implicit processes, and the challenges involved in assessing implicit processes' causal role in determining health behaviors. (PsycINFO Database Record

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.157
GPT teacher head0.506
Teacher spread0.349 · 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

Citations49
Published2016
Admission routes1
Has abstractyes

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