Towards a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Quebec.
Bibliographic record
Abstract
Abstract Objectives: The primary objective of this project was to develop a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Quebec (QCnPI-Index), Canada. The resulting and open access database systematically categorizes, multiple non-pharmaceutical interventions implemented to mitigate the spread of COVID-19 in the 17 administrative regions (AR) of the province of Quebec in the form of an index. Data description: Data represent interventions and groups of interventions implemented during the COVID-19 period in Quebec. They are a compilation of policies, guidelines, and governmental interventions related to COVID-19, considering temporal and geographical dimensions. Data were collected, from March 2020 to July 2022, for all 17 ARs of Quebec using dates and ARs as unit of analysis. Data were collected and coded by an interdisciplinary research team. Contribution: This quantitative instrument offers the necessary granularity for nuanced spatial and temporal studies within the province of Quebec, using AR, for instance, as unit of analysis. With this database, pre-, during-, and post-COVID periods can thus be better analyzed. Additionally, the innovative methodologies employed for data collection, coding, and weighting offer valuable insights that may have broader applications in public health, epidemiology, and other research domains. The QCnPI-Index could be instrumental for public health, epidemiology, and transportation researchers investigating the multifaceted impacts of non-pharmaceutical interventions on various societal domains, such as road safety, alcohol and cannabis consumption, and/or mental health, in the province of Quebec.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".