A review of theoretical models of health information seeking on the web
Bibliographic record
Abstract
Purpose By selectively reviewing theory‐driven survey studies on internet health information seeking, the paper aims to provide an informal assessment of the theoretical foundations and research methods that have been used to study this information behavior. Design/methodology/approach After a review of the literature, four theory‐driven quantitative survey studies are analyzed in detail. Each study is examined in terms of: theoretical framework; research variables that form the focus of the study; research design (sampling, data collection and analysis); and findings and results of hypothesis testing and model testing. The authors then discuss the theoretical models and analytical methods adopted, and identify suggestions that could be helpful to future researchers. Findings Taken as a whole, the studies reviewed point strongly to the need for multidisciplinary frameworks that can capture the complexity of online health information behavior. The studies developed theoretical frameworks by drawing from many sources – theory of planned behavior, technology acceptance model, uses and gratifications, health belief model, and information seeking models – demonstrating that an integration of theoretical perspectives from the health sciences, social psychology, communication research, and information science, is required to fully understand this behavior. The results of these studies suggest that the conceptual models and analytical methods they adopted are viable and promising. Many relationships tested showed large effect sizes, and the models evaluated were able to account for between 23 and 50 percent of the variance in the dependent variables. Originality/value The paper represents a first attempt to compare, evaluate, and to a degree synthesize the work that has been done to develop and test theoretical models of health information seeking on the web.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".