Co-designing Towards Community Health Outcomes: The role of social innovation labs and organizations during the COVID-19 pandemic
Bibliographic record
Abstract
In this Major Research Project (MRP), I highlight community-driven innovations in response to COVID-19, focusing on case studies from social innovation labs and organizations (referred to as “Labs”) in Ontario, Canada. These Labs, which address the social determinants of health both within and outside the formal healthcare system, played a key role in supporting, supplementing, and scaling grassroots emergency response efforts aimed at reducing health inequities during the pandemic. Drawing on interviews with 10 social innovation practitioners and designers, this MRP argues that the role and value of Labs has evolved since 2020. Looking ahead, Labs can play a pivotal role in addressing ongoing gaps and crises in Canada’s health system through building ‘relational infrastructure.’ Specifically, Labs can (1) act as connective tissue in a siloed system, (2) engage community partners in dialogue and collaborative processes to build bridges between health system actors, and (3) centre community voices in developing strategies and solutions through equitable co-design.
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 | 0.049 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.040 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".