Learning from the Pandemic: Alberta’s Need for a Comprehensive & Inclusive Long COVID Plan
Bibliographic record
Abstract
On March 11, 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a global pandemic (World Health Organization 2020). Two years later, we are seeing a new phenomenon: long COVID. An individual is diagnosed with long COVID if they have various symptoms including but not limited to loss of taste or smell, shortness of breath or a lingering cough, and mental health concerns for eight weeks post acquiring COVID-19 (Park Integrative Health 2021). As many as 118,894 to 362,629 Albertans will experience long COVID (Alberta 2022b; Smith 2022). However, some groups were disproportionately affected by COVID-19, and subsequently long COVID. Immigrants are one such group due to poor work conditions, not knowing their rights as workers and language barriers. Alberta lacks a long COVID strategy that provides financial, social, and legal protections for those experiencing long COVID. Most importantly, Alberta needs a long COVID strategy that is inclusive in that it meets the needs of underserved populations such as immigrants. This strategy must have input from policy makers as well as epistemic communities. As well, data acquisition, specifically when it comes to race-based data needs to be in the forefront of this strategy. Alberta can model its program after other jurisdictions, notably the United Kingdom, that have strategies in place. The strategy should be informed by the efforts by various members of Alberta’s epistemic community including but not limited to Healing Centred Cooperative’s music and breathwork program. The pandemic brought with it restrictions, changes, and even loss. Nevertheless, there was a silver lining in the pandemic. We can use what we learned to improve rehabilitation services for Albertans, especially those underserved like immigrants, in the long term.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.024 | 0.030 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".