Implicit processes in health psychology: Diversity and promise.
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".