Living with deafblindness during COVID-19: An international webinar to facilitate global knowledge translation
Bibliographic record
Abstract
Arguably, individuals living with deafblindness are among the hardest hit by the effects of the corona virus disease of 2019 (COVID- 19), given the importance of the sense of touch for their ability to interact with the world. To address this challenge, it is imperative to facilitate the implementation of evidence- and experience-based recommendations, and to improve knowledge translation on a global scale. Deafblind International organized a webinar to provide a platform where participants could exchange experiences and solutions to overcome the challenges created by the arrival of COVID-19, in order to facilitate information exchange among stakeholders in deafblindness during this pandemic. We present an overview of its content here and place the summarized themes in context with existing research literature. Abstract submission was open for 4 weeks in May 2020, resulting in 30 submissions from 13 countries across 5 continents. Of the 26 presenter teams, 9 (35%) had a co-presenter that was living with deafblindness themselves. The number of individual participants across all sessions ranged from 55 to 140 ( M = 98), with a total of 3709 session registrations overall, and the organizers estimate a total attendance of around 400 participants. Based on extensive field notes taken during the webinar, and repeated viewing of the recordings, qualitative description allowed the team to synthesize eight principal themes across the event: access to information, communication, service accessibility, adaptations to service delivery, online safety and security, physical distancing, mental health and research. The first Deafblind International webinar was able to fill an important gap by bringing together a variety of stakeholders in deafblindness across the globe. The event created a sense of group membership and peer support, brought the participants, researchers, the professionals as well as their service agencies closer together and generated a sense of hope and collaboration.
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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 | 0.058 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".