Workshop (Health Services, Economics and Policy Change) ID 2012040
Bibliographic record
Abstract
Background/Objectives According to the WHO nearly one billion people worldwide live with some form of disability and one fifth of those have significant difficulty functioning as a result. Climate change is a reality and the impacts on healthcare and the care of our communities is significant. From food and water supply, housing emergency preparedness and safety to the effects on our health, climate change has a disproportionate impact on persons with disabilities. The purpose of this workshop is to provide attendees with a review of the issues that climate change has created for persons with disabilities with a focus on traumatic and non-traumatic spinal cord injury. We will then move to a group discussion on what we can do individually and as health systems to address these challenges and make sure the rights and safety of persons with disabilities are protected as we navigate our world with Climate Change. Methods/Overview We will use powerpoint slides, polls and group discussion to explore this topic. Results This workshop will further educate SCI experts nationally/internationally and collect their thoughts and ideas on climate impact on persons with disabilities. Conclusions Our hope is that this information will help inform a more formal position statement from the CSCIRA.
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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.536 | 0.133 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".