"Heard you got a great pandemic plan, would you mind sharing it?": COVID-19 Pandemic Planning and Response in Local Governments in British Columbia
Bibliographic record
Abstract
The COVID-19 pandemic has pressured governments to plan and implement policies to protect their citizens and economies. In British Columbia (B.C.), all local governments needed to plan and respond to the pandemic emergency to some degree. However, due to the variations in population, region, and resource capacity, there may be a number of discrepancies between local governments. Using key informant interviews with emergency management staff from local governments across B.C., this thesis aims to identify how local governments in B.C. used pandemic planning documents to develop policies to respond to the COVID-19 pandemic. The analysis revealed that the majority of participants viewed pandemic planning documents as not critical to the successful implementation of policies. The analysis also identified what the participants believed worked well and did not work well when planning and responding to the pandemic with respect to collaboration, communication, staff impacts, digital infrastructure, and financial impacts. The thesis concludes by recommending that local governments develop a flexible plan, establish collaborative networks with target groups, create communication strategies with higher levels of government, and regularly review and update digital infrastructure.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".