Strategies for crowdsourcing hearing health information: a comparative study of educational programs and volunteer-based campaigns on Wikimedia
Bibliographic record
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 volunteer-based Wikimedia outreach campaign with university course Wikipedia assignments (both focused on improving hearing health content in Wikimedia's public digital knowledge archives), in terms of the reach of the contributions and the extent of the participants' input. A secondary objective was to examine the feasibility and the implementation of the different strategies. METHODS: The research team partnered for the (1) coordination of improvements in hearing and healthcare content through educational programs using Wikimedia platforms, (2) participation in the global campaign Wiki4WorldHearingDay2023 and (3) evaluation of the proposed strategies. Metrics used in the comparison of the two strategies included the number of articles edited, number of views of the edited articles (as reach) and the extent of edits, captured as the number of words. The feasibility evaluation included assessing recruitment success and the implementation of the proposed plan among faculty, students from various university programs, and volunteers representing different constituencies. RESULTS: The effort increased the availability of quality plain language information on hearing conditions and hearing care. Both strategies demonstrated to be feasible by their success in recruiting participants who contributed to the effort and by measurable outputs as edits. The contribution of content to Wikimedia platforms as part of education activities provided a more robust result. Wiki4WorldHearingDay2023 145 participants (78 from educational programs) contributed 167,000 words, 258 + references and 140 images to 322 Wikipedia articles (283 existing and 39 new ones), which were viewed 16.5 million times. Contributions occurred in six 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 elements that contributed to the success of the studied strategies include an impact topic, coordination with educational programs, international multidisciplinary collaborations, the dissemination of the initiative in several platforms, connection with a robust local Wikimedia affiliate, and the use of a technical infrastructure that provides metrics and coordination mechanisms.
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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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".