Public Healthcare Procurement Strategies in Response to the COVID-19 Pandemic: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic posed unprecedented public healthcare procurement challenges. The objective of this review was to identify and characterise the scope of the literature on public procurement strategies for healthcare supplies during the COVID-19 pandemic (2019-2023) in relation to the public procurement contexts, systems, and processes and methods (the public procurement ecosystem) worldwide. METHODS: We performed a scoping review of governmental strategies for the procurement of medical equipment, personal protective equipment (PPE), or medications related to the COVID-19 pandemic. Extracted data were mapped to the fields of the public procurement ecosystem. We used inductive thematic analysis to derive within-field themes, and subsequently, cross-cutting themes through which we structured a narrative synthesis. RESULTS: 1909 unique studies were identified through a systematic search, of which 89 met the inclusion criteria. One hundred and ten themes were derived from the extracted data within the 21 fields of the public procurement ecosystem, and from these, 10 cross-cutting themes were identified which served to structure the narrative synthesis. It was clear in this literature that the scale and impact of the COVID-19 pandemic required governments to act well outside of the public procurement processes and methods themselves, to procure and distribute the required supplies. Notwithstanding the significant attention to contextual and system-level responses, there were significant responses at the procurement process and methods level, including rapid and temporary expedited procurement processes and longer-term strategic procurement responses. CONCLUSION: This scoping review of public procurement strategies during the COVID-19 pandemic has demonstrated a focus of the literature not only on the public procurement processes and methods themselves, but also on governmental actions to adapt both structures of public procurement systems and conditions within broader environmental contexts to facilitate procurement goals.
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 | 0.022 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".