Early Learning from the Healthy End of Life Project (HELP) Ottawa in the Context of COVID-19
Bibliographic record
Abstract
Abstract Healthy End of Life Project (HELP) Ottawa is a community-based participatory research initiative which is based in four community sites in Ottawa, Ontario (Canada) (2 community health centers, and 2 faith communities). Focused on the needs of people who are frail, living with advanced illness, and their caregivers, including the needs of people who are bereaved, HELP Ottawa strives to, 1)strengthen informal and community social networks, organizational cultures and linkages across local health and social care services; 2)create a community culture that supports people to build social care networks to be able to ask for and accept help, and, 3)mobilize and prepare community members to be confident and capable of offering and providing help to people in their communities. Unfolding within the context of COVID-19, each HELP Ottawa site has found ways to mobilize, adapt and respond to lockdowns, quarantines, increased isolation and altered needs and services. Drawing on 89 initial consultations, followed by 111 interviews and 16 focus group participants (n=164), qualitative findings speak to the heightened grief and fear experienced within each site during the COVID-19 pandemic, and the multiple costs of severed ‘essential’ links. Critically highlighted is the need to build and sustain social supports and connection through everyday and local means while also integrating technology and online communication. Further apparent are the critical questions that need to be asked about how compassionate communities, and communities at large, can prepare for and respond to current and future waves of COVID-19.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".