A global comparative analysis of the the inclusion of priority setting in national COVID-19 pandemic plans: A reflection on the methods and the accessibility of the plans
Bibliographic record
Abstract
BACKGROUND: Despite the swift governments' response to the COVID-19 pandemic, there remains a paucity of literature assessing the degree to which; priority setting (PS) was included in the pandemic plans and the pandemic plans were publicly accessible. This paper reflects on the methods employed in a global comparative analysis of the degree to which countries integrated PS into their COVID-19 pandemic plans based on Kapiriri & Martin's framework. We also assessed if the accessibility of the plans was related to the country's transparency index. METHODS: Through a three stage search strategy, we accessed and reviewed 86 national COVID-19 pandemic plans (and 11 Canadian provinces and territories). Secondary analysis assessed any alignment between the readily accessible plans and the country's transparency index. RESULTS AND CONCLUSION: 71 national plans were readily accessible while 43 were not. There were no systematic differences between the countries whose plans were readily available and those whose plans were 'missing'. However, most of the countries with 'missing' plans tended to have a low transparency index. The framework was adapted to the pandemic context by adding a parameter on the need to plan for continuity of priority routine services. While document review may be the most feasible and appropriate approach to conducting policy analysis during health emergencies, interviews and follow up document review would assess policy implementation.
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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 | 0.083 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".