The brain tumor not-for-profit and charity experience of COVID-19: reacting and adjusting to an unprecedented global pandemic in the 21st century
Bibliographic record
Abstract
BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has affected individuals as well as disease-specific brain tumor organizations. These organizations around the world exist to address unmet needs for patients and caregivers they serve. The direct impact of the pandemic on these organizations constitutes significant collateral damage. In order to better understand the effects of the COVID-19 pandemic on brain tumor organizations, the International Brain Tumour Alliance (IBTA) carried out an international survey to identify organizational changes induced by the virus and approaches adopted to address challenges. METHODS: A 37-question online survey consisting of categorical and qualitative questions was developed and circulated to 130 brain tumor organizations across the world. Seventy-seven organizations from 22 countries completed the survey (59% return rate). Descriptive statistics and content analysis were used to present the results. RESULTS: Responses fell into the following 3 categories: (1) organizational characteristics, (2) impact of COVID-19 on services, and (3) COVID-19 impact on financial and human resources within organizations. Although organizational characteristics varied, common concerns reported were activity disruption which impacted organizations' abilities to offer usual services and challenges to sustaining funding. Both financial and human resources were stressed, but integral adaptations were made by organizations to preserve resources during the pandemic. CONCLUSIONS: Although brain tumor organizations have been impacted by the COVID-19 pandemic, organizations quickly adjusted to this unprecedented global healthcare crisis. Nimble reactions and flexibility have been vital to organization sustainability. Innovative approaches are required to ensure organizations remain viable so that needs of brain tumor community at large are met.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".