Capturing and Documenting the Wider Health Impacts of the COVID-19 Pandemic Through the Remember Rebuild Saskatchewan Initiative: Protocol for a Mixed Methods Interdisciplinary Project
Bibliographic record
Abstract
BACKGROUND: In the Canadian province of Saskatchewan, the global COVID-19 pandemic appeared amidst existing social health challenges in food insecurity, housing precarity and homelessness, poor mental health, and substance misuse. These chronic features intersected with the pandemic, producing a moment in time when the urgency of COVID-19 brought attention to underlying shortcomings in public health services. OBJECTIVE: The objectives of the program of research are (1) to identify and measure relationships between the pandemic and wider health and social impacts, namely, food insecurity, housing precarity and homelessness, and mental health and substance use in Saskatchewan, and (2) to create an oral history of the pandemic in Saskatchewan in an accessible digital public archive. METHODS: We are using a mixed methods approach to identify the impacts of the pandemic on specific equity-seeking groups and areas of social health concern by developing cross-sectional population-based surveys and producing results based on statistical analysis. We augmented the quantitative analysis by conducting qualitative interviews and oral histories to generate more granular details of people's experiences of the pandemic. We are focusing on frontline workers, other service providers, and individuals within equity-seeking groups. We are capturing digital evidence and social media posts; we are collecting and organizing key threads using a free open-source research tool, Zotero, to trace the digital evidence of the pandemic in Saskatchewan. This study is approved by the Research Ethics Board at the University of Saskatchewan (Beh-1945). RESULTS: Funding for this program of research was received in March and April 2022. Survey data were collected between July and November 2022. The collection of oral histories began in June 2022 and concluded in March 2023. In total, 30 oral histories have been collected at the time of this writing. Qualitative interviews began in April 2022 and will continue until March 2024. Survey analysis began in January 2023, and results are expected to be published in mid-2023. All data and stories collected in this work are archived for preservation and freely accessible on the Remember Rebuild Saskatchewan project's website. We will share results in academic journals and conferences, town halls and community gatherings, social and digital media reports, and through collaborative exhibitions with public library systems. CONCLUSIONS: The pandemic's ephemeral nature poses a risk of us "forgetting" this moment and the attendant social inequities. These challenges inspired a novel fusion among health researchers, historians, librarians, and service providers in the creation of the Remember Rebuild Saskatchewan project, which focuses on preserving the legacy of the pandemic and capturing data to support an equitable recovery in Saskatchewan. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46643.
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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 | 0.090 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.015 |
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".