Building Equitable Patient Partnerships during the COVID-19 Pandemic: Challenges and Key Considerations for Research and Policy
Bibliographic record
Abstract
The unequal social and economic burden of the COVID-19 pandemic is evident in racialized and low-income communities across Canada. Importantly, social inequities have not been adequately addressed and current public policies are not reflective of the needs of diverse populations. Public participation in decision-making is crucial and there is, therefore, a pressing need to increase diversity of representation in patient partnerships in order to prevent the further exclusion of socially marginalized groups from research and policy making. Deliberate effort and affirmative action are needed to meaningfully engage and nurture diverse patient partnerships by broadening the scope of the patient community to include excluded or underrepresented individuals or groups. This will help us co-develop ways to enhance access and equity in healthcare and prevent the systematic reproduction of structural inequalities that have already been heightened by the COVID-19 pandemic.
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 | 0.155 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.032 | 0.033 |
| Open science | 0.009 | 0.044 |
| Research integrity | 0.023 | 0.027 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".