Wikipedia: an unexplored resource for understanding consumer health information behaviour in library and information science scholarship
Bibliographic record
Abstract
Purpose To date, health information behaviour (HIB) models have not been applied to an exploration of Wikipedia as a consumer health information resource. Wikipedia has been situated and is well established as a valuable resource for the general layperson wishing to learn more about their health or the health of a loved one. This paper aims to identify an approach to exploring the role of Wikipedia in consumer health information behaviour (CHIB) that is grounded in a conceptual framework from the library and information science (LIS) discipline. Design/methodology/approach The author draws on current HIB models and relevant theories from existing LIS literature and applies them to propose a new definition of CHIB. The author uses this definition to frame Wikipedia as an unexplored consumer health information resource in the LIS scholarship and suggests future directions for placing such investigations within a conceptual framework from LIS. Findings The paper finds that Longo's expanded conceptual model of health information-seeking behaviour (ECMHISB) could be valuable and useful for the exploration of CHIB in relation to Wikipedia's health and medical content. Due to Wikipedia's online nature, research framed by these models must acknowledge and take under consideration the digital divide phenomenon and various factors that influence an individual's place within it. Research limitations/implications This work builds a foundation upon which future research into the role of Wikipedia's health and medical content in CHIB can be grounded. Using Longo's model, future research might provide insight into who Wikipedia is helping and who it has left behind. LIS scholars, practicing health librarians and perhaps health workers stand to gain a deeper understanding of the potential influence of Wikipedia's health information on its consumers. Originality/value For LIS scholars, this paper is novel in the fact that a HIB model has not yet been applied to the study of Wikipedia's health content. This paper provides a foundation for this research.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".