Tracing and measuring the COVID-19 Colombian vaccination network
Bibliographic record
Abstract
The COVID-19 vaccination process in Colombia has been a major challenge not only in terms of public health but also in terms of supply chain management and logistics processes. To support the monitoring of these processes and associated decision-making, a dashboard was designed in Google Data Studio focused on analyzing the progress of COVID-19 vaccination and its logistics efficiency. This article describes the design and implementation of the dashboard using a design science approach and discusses the main lessons learned. During its development, four major challenges were identified: the search for and availability of data sources, the definition and standardization of metrics, the extraction of data in different formats; and finally, the validation of the metrics. Despite these challenges, the dashboard became a source of information for different stakeholders in the Colombian COVID-19 vaccination network, facilitating the monitoring of key performance indicators (KPIs), supporting decision-making, and policy evaluation. This reaffirms the importance of having open information to generate knowledge for both public and private entities as well as for the public. The main contribution of this work is the definition and standardization of the KPIs and it is therefore expected that this experience will serve as an insightful input for designing mass vaccination strategies.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".