Motivations for Contributing to Health-Related Articles on Wikipedia: An Interview Study
Bibliographic record
Abstract
BACKGROUND: Wikipedia is one of the most accessed sources of health information online. The current English-language Wikipedia contains more than 28,000 articles pertaining to health. OBJECTIVE: The aim was to characterize individuals' motivations for contributing to health content on the English-language Wikipedia. METHODS: A set of health-related articles were randomly selected and recent contributors invited to complete an online questionnaire and follow-up interview (by Skype, by email, or face-to-face). Interviews were transcribed and analyzed using thematic analysis and a realist grounded theory approach. RESULTS: A total of 32 Wikipedians (31 men) completed the questionnaire and 17 were interviewed. Those completing the questionnaire had a mean age of 39 (range 12-59) years; 16 had a postgraduate qualification, 10 had or were currently studying for an undergraduate qualification, 3 had no more than secondary education, and 3 were still in secondary education. In all, 15 were currently working in a health-related field (primarily clinicians). The median period for which they have been an active editing Wikipedia was 3-5 years. Of this group, 12 were in the United States, 6 were in the United Kingdom, 4 were in Canada, and the remainder from another 8 countries. Two-thirds spoke more than 1 language and 90% (29/32) were also active contributors in domains other than health. Wikipedians in this study were identified as health professionals, professionals with specific health interests, students, and individuals with health problems. Based on the interviews, their motivations for editing health-related content were summarized in 5 strongly interrelated categories: education (learning about subjects by editing articles), help (wanting to improve and maintain Wikipedia), responsibility (responsibility, often a professional responsibility, to provide good quality health information to readers), fulfillment (editing Wikipedia as a fun, relaxing, engaging, and rewarding activity), and positive attitude to Wikipedia (belief in the value of Wikipedia). An additional factor, hostility (from other contributors), was identified that negatively affected Wikipedians' motivations. CONCLUSIONS: Contributions to Wikipedia's health-related content in this study were made by both health specialists and laypeople of varying editorial skills. Their motivations for contributing stem from an inherent drive based on values, standards, and beliefs. It became apparent that the community who most actively monitor and edit health-related articles is very small. Although some contributors correspond to a model of "knowledge philanthropists," others were focused on maintaining articles (improving spelling and grammar, organization, and handling vandalism). There is a need for more people to be involved in Wikipedia's health-related content.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".