Exploratory study: Health promotion through Wikipedia outreach and educational activities
Bibliographic record
Abstract
Abstract Background Several health institutions developed strategies to improve health content on Wikimedia platforms, given their unparalleled reach. The objective of this study was to compare an online a volunteer-based Wikimedia outreach campaign and Wikipedia university course assignments in terms of the reach of the contributions and evaluate the extent of the students' input. Methods In 2022, researchers from seven Brazilian universities and a Canadian university, in coordination with the National Institute for Occupational Safety and Health, the World Health Organization, the Ronin Institute, and Wiki Movimento Brasil, received a grant from the State of São Paulo (Brazil) that supported the 1) coordination of improvements in hearing and healthcare content through educational programs using Wikimedia platforms and 2) participation in the global campaign Wiki4WorldHearingDay2023. We examined the feasibility and the implementation of the two strategies and compared the contributions from those enrolled in educational activities versus volunteer activities from Wikipedia editors to a global campaign. Results The strategy was demonstrated to be feasible. It increased the availability of quality plain language information on hearing conditions and hearing care. By May 1, 2023, Wiki4WorldHearingDay2023, 145 participants (78 from educational programs) had contributed 167,000 words, 259 + references and 140 images to 322 Wikipedia articles (283 existing and 39 new ones), which were viewed by 16.5 million readers. Contributions occurred in 6 languages. Edits in Portuguese, mainly by those involved in educational programs, led the number of articles (226 or 70.2%) that were expanded or created during the 5-month tracking period. Conclusions The crowdsourcing of expertise and knowledge is relevant for public health. This study’s approach can be applied in other contexts. In addition to the coordination with educational programs, the elements that contributed to the success of these initiatives include an impact topic, international collaborations, the connection with a robust local Wikimedia affiliate, and the use of a technical infrastructure that gives us metrics and coordination mechanisms. The partnerships, the dissemination of the work in several platforms, the participation of multidisciplinary teams, and the availability of resources through institutional support and funding were additional elements that contributed to the success of these initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".