Bibliographic record
Abstract
Over six percent of the world's population has disabling hearing loss.1 Across the globe, it can take years for those with hearing problems to seek care. Even when care is sought, the prescribed interventions are not often adopted.2 This issue has motivated the World Health Organization to coordinate World Hearing Day (WHD), an annual event that aims “to raise awareness on how to prevent deafness and hearing loss and promote ear and hearing care across the world.”3hearing health, World Hearing DayThe Centers for Disease Control and Prevention argues that “plain language makes it easier for everyone to understand and use health information,”4 emphasizing its commitment to making high-quality information freely accessible to the public.5 As health articles on Wikipedia are collectively read more than 150 million times per month,6 one approach the National Institute for Occupational Safety and Health is taking to communicate research findings involves improving health content on Wikipedia.7 That includes the adoption by university programs of the WikiEducation Foundation https://wikiedu.org/8 platform to train students in Wikipedia editing. Through this mechanism, students contribute evidence-based content to Wikipedia as a class assignment.9 This year, NIOSH proposed and developed the online event Wiki4WorldHearingDay2019 http://bit.ly/2DzSdD410 to facilitate the improvement of Wikipedia content related to hearing, hearing health services, hearing testing, and preventive and rehabilitative interventions. The platform provided guidance and allowed anyone with access to a computer and the internet to participate in the campaign. Several institutions joined by either promoting the event or contributing content to Wikipedia, including the National Center for Environmental Health https://www.cdc.gov/nceh/, the National Center on Birth Defects and Developmental Disabilities https://www.cdc.gov/ncbddd/index.html, the Hearing Center of Excellence https://hearing.health.mil/, the French National Research and Safety Institute for the Prevention of Occupational Accidents http://en.inrs.fr/, the International Society of Audiology https://www.isa-audiology.org/, the Acoustical Society of America https://acousticalsociety.org/, the American Academy of Audiology https://www.audiology.org/, and Hear in Cincinnati https://www.linkedin.com/company/hear-in-cincinnati. Participants from Cochrane, Cochrane Ear, Nose and Throat https://www.cochrane.org/, and university programs in the United States, Canada, United Kingdom, Brazil, and South Africa also contributed expert content, which led to content being added in English, Portuguese, French, Spanish, Italian, and Swedish. Through the Wikimedia outreach dashboard http://bit.ly/2KLLUmA,11 we were able to monitor the contributions and their reach with a great level of detail. Tracking data from the platform launch on Jan. 21, 2019, to March 31, 2019, showed that 74,000 words were contributed to 90 existing and seven new Wikipedia articles, and 21 images were donated to the open access repository WikiCommons. These pages received more than 2 million views (the 66 editors must be delighted to see this level of readership!). Crowdsourcing of expertise and knowledge is relevant for public health. The transparency of editorial processes and engagement on Wikipedia make it easier for experts to address any concerns they might have over participation. The heightened level of review of Wikipedia health articles is only possible due to the generous dedication of contributors who are health professionals and collaborative ventures, such as Wiki4WorldHearingDay2019, with scientific associations and agencies.12 We encourage organizations and institutions across the world to join current and future efforts.13 The breadth and accuracy of the Wikipedia coverage of the sciences vary widely. Awareness campaigns present unique opportunities to make hearing-related content one of the better developed domains within Wikipedia while providing quality information to the global audience in a place where they actually look for it. Thoughts on something you read here? Write to us at [email protected]
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".