Two Public Health Crises, Two Narratives: An Analysis of How Policymakers Have Managed British Columbia's COVID-19 Pandemic and Potential Implications for the Ongoing Overdose Crisis
Bibliographic record
Abstract
Canadians take great pride in their social values such as human and civil rights, universal health care and good government. In response to the COVID-19 pandemic, federal and provincial leadership teams forged new partnerships via shared focus, voluntariness, jurisdictional respect, and lowering of barriers. In our analysis focusing on the Province of British Columbia, we compare and contrast how leadership and politics have impacted the response to COVID-19 vs. the response to B.C.'s concurrent public health emergency, the overdose crisis. We argue that these dual epidemics are framed differently in the public discourse, and that a significant disparity emerges in how the two public health emergencies have been handled at every level of government. We further posit that constructing the narrative around a communicable disease outbreak such as COVID-19 is easier than for the overdose crisis, in large part because COVID-19 impacts every person whereas the overdose crisis is perceived to have a narrow impact on the population. We use three key communications indicators in our analysis: a) the primary groups that messaging from leadership needed to reach; b) the programs and initiatives that leadership needed to ensure receive broad dissemination; and c) the messaging and tone required to achieve the desired impact to encourage societal change. On the basis of our analysis, we conclude that Canada needs to be better at building the types of supports it has created to manage the COVID-19 crisis in order to also support individuals who are immersed in the overdose crisis. Many of the policy and communication decisions and insights learned through the COVID-19 pandemic can, and ought to, be put into effect to mitigate the ongoing overdose crisis in B.C. and beyond. Examples include: consistent messaging that emphasizes respect for all and reflects determination from our political leaders as they work together to change the narrative and enact policy change. COVID-19 has shown us that if we are determined and focused, even if we occasionally run into obstacles, we can move the dial forward to mitigate—and perhaps even eliminate—a health crisis.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.050 | 0.022 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| 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".