The role of information in health behavior: A scoping study and discussion of major public health models
Bibliographic record
Abstract
Information interventions that influence health behavior are a major element of the public health toolkit and an area of potential interest and investigation for library and information science (LIS) researchers. To explore the use of information as a concept within dominant public health behavior models and the manner in which information practices are handled therein, we undertook a scoping study. We scoped the use of “information” within core English‐language health behavior textbooks and examined dominant models of health behavior for information practices. Index terms within these texts indicated a lack of common language around information‐related concepts. Nine models/theories were discussed in a majority of the texts. These were grouped by model type and examined for information‐related concepts/constructs. Information was framed as a “thing” or resource, and information practices were commonly included or implied. However, lack of specificity regarding the definition of information, how it differs from knowledge, and how context affects information practices make the exact role of information within health behavior models unclear. Although health information interventions may be grounded in behavioral theory, a limited understanding of the ways information works within people's lives hinders our ability to effectively use information to improve health. By the same token, information scientists should explore public health's interventionist approach.
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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 | 0.094 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.027 | 0.031 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".