Bibliographic record
Abstract
The COVID-19 pandemic has exposed significant fragilities in the current capacity and function of global healthcare supply chains. During the first wave of the pandemic, long, undiversified, and lean global supply chains were destabilized by a massive surge in demand for care, that required high volumes of critical health products for care delivery (Snowdon, Saunders & Wright, 2021). China, the primary manufacturer of a number of critical health products and the first site of a COVID-19 outbreak, temporarily shuttered its manufacturing capacity. As a result, there were severe product shortages across every global health system. Manufacturers were unable to rapidly scale their production capacity to meet the sudden and dramatic increase in the demand for critical products, which resulted in a destabilizing “ripple effect” across global healthcare supply chains. The COVID-19 pandemic, and the surge in supply demands it created, exposed the fragility that rapidly destabilized these global healthcare supply chains. This destabilization of healthcare supply chains impacted every jurisdiction in Canada and touched the lives of healthcare workers, patients, citizens, and non-permanent residents (such as temporary foreign workers).
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".