Bibliographic record
Abstract
As the coronavirus disease 2019 (COVID-19) pandemic persisted through the spring and fall, Ovarian Cancer Canada worked to quickly transition its largest annual fundraiser—the Walk of Hope, held during September in 35 cities—to a virtual format. Instead of gathering for in-person events filled with cheering onlookers, participants walked alone or with just a few relatives and friends in their own neighborhoods to raise money for the nonprofit. Ovarian Cancer Canada's quick pivot is just 1 example of how cancer advocacy organizations worldwide have had to adapt nearly overnight to the challenges of the COVID-19 pandemic. Moreover, with significant declines in corporate sponsorships and individual donations and with the cancellation of major in-person fundraising events, some organizations have been forced to make sizable cuts to their funding and services. “Nearly all of [the groups] are projecting a drop in income—some up to 50%,” she says. “These organizations had to change on a dime the way they operate, in terms of how they provide services, and those that fund research have all been negatively impacted in terms of how much they can continue to invest.” According to MacKay, the goal of the survey was to highlight the hardship that patient advocacy and cancer organizations are experiencing and the value that they bring, particularly with regard to providing direct services to patients. Amid fears that the fall could bring another spike in COVID-19 cases, the coalitions plan to conduct a follow-up survey in December to determine how these organizations have fared 6 months after the initial survey. “Our concern is a second wave and lack of preparation in the health care system in terms of cancer services, as well as fatigue among health care workers,” MacKay says. “We need to be in front of it.” Cancer organizations globally are focusing on the areas of greatest need, and in some cases, that means they have had to reduce their advocacy activities to bolster direct patient services. Over time, those reductions could have serious implications, MacKay says. Still, she notes, some positives for the cancer community have arisen from the COVID-19 crisis. For example, the coalition of global cancer organizations that conducted the survey will likely collaborate on future, shared areas of concern. In addition, the use of telephone support groups and other virtual events has enabled more people to participate than ever before. “I'm very proud of the sector I'm working in,” MacKay says, noting that her colleagues have been innovative and resourceful in a difficult time. “I feel strongly about acknowledging the huge contribution that cancer patient advocacy groups make, and I think local and national governments should be thinking about them as a crucial part of the health economy.” Meanwhile, leaders of organizations such as Ovarian Cancer Canada are continuing to work hard to educate and reach out to their communities. “Cancer didn't go away just because of COVID-19, and we need to be a loud voice to make sure people are paying attention to their health and symptoms,” Baugh says.
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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".