COVD-11. THE BRAIN TUMOR AND NOT FOR PROFIT AND CHARITY EXPERIENCE OF COVID 19: REACTING AND ADJUSTING TO AN UNPRECEDENTED GLOBAL PANDEMIC IN THE 21ST CENTURY
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has not only affected individuals, but also disease specific not-for-profits and charities. Brain tumor not-for-profit and charitable organizations around the world exist in all shapes and sizes, and address unmet needs of the patients and caregivers they serve. The International Brain Tumor Alliance(IBTA) carried out an international survey to identify organization changes brought about by the virus and the approaches adopted to address operational challenges created by COVID-19. A 37-question survey was sent across the world. In total, 77 organizations from 22 countries responded. Descriptive statistics and content analysis were used to present RESULTS: Responses fell into three categories: 1) organizational characteristics, 2) the impact of COVID-19 on services, and 3) how COVID-19 has affected the financial and human resources in these organizations. Although organizational characteristics vary widely, common concerns reported across organizations were primarily: a) the disruption of activities which impacted organizations’ abilities to offer their usual services and b) challenges to sustaining funding. Although brain tumor organizations have been impacted by the COVID-19 pandemic, organizations quickly adjusted to this unprecedented global healthcare crisis.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".