“They wanted to, but they just couldn’t get there”: GBA + implementation and gaps during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
PURPOSE: To improve understanding of the barriers and enablers to implementing gender and intersectional analysis during the COVID-19 pandemic in Canada. METHODS: We conducted a policy document analysis (n = 70) of equity-focused policies of the Canadian government published between March 2020 and August 2023. This analysis was complemented with 16 semi-structured key informant interviews with federal policy actors and leadership of civil society organizations. RESULTS: Pandemic policy documents demonstrated multiple commitments to address pandemic related inequities, with key informants describing collaborative approaches to implementing these policies, but also limits in terms of the urgent and diffused nature of pandemic response. Implementation gaps related to accessible information, health services and vaccinations were noted and attributed to a reliance on civil society actors who lacked sufficient and sustainable resources, and the behaviors of priority populations whose capacity to comply was limited by the same inequities the policies sought to address. CONCLUSION: The Canadian federal government made concerted efforts to address the needs of a range of priority populations and equity issue areas within its pandemic response, with mixed results. Having a pre-established framework to guide implementation and related relationships overcame some of the urgency challenges related with pandemic response. However, implementation gaps reflected preexisting inequities shaped by broader economic, social and political systems which were infrequently addressed in pandemic policies. There is a need for greater understanding of policy implementation gaps during emergency and crisis response.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".