Private consulting firms’ intervention in public health policymaking: An exploratory review
Bibliographic record
Abstract
Context. While there is ample research in the social sciences on the role of private consulting firms in public policy, there is little information about their intervention in managing public health crises and epidemics. The COVID-19 pandemic revealed how much public administrations across the globe have been using these firms. The purpose of this exploratory review of the scientific literature is to identify research on the involvement of these firms in governing epidemics and health crises since 2000. Methods. This review investigates the following question: what research evidence about the role of these firms is there, and what research methods and analytical categories are used? Following the stages of the PRISMA methods, we identified 24 references since 2000. Findings. We classified authors’ analyses of the role played by those firms using three analytical categories: the management approach, the consultocracy phenomenon and the phenomenon of elite hybridization. Only two references were explicitly related to the work of consulting firms in the context of epidemics (e.g. COVID-19). The others focused on public health reforms. This finding confirms the scarcity of research evidence on the role played by consulting firms in the management of epidemics. Conclusions. This review reports on a blind spot of the scientific literature and calls for additional empirical research. Points for practitioners Consulting firms’ intervention during epidemics remains a blind spot of academic research. The COVID-19 crisis prompted a significant growth of consulting firms’ intervention in health policymaking. Three analytical categories can be useful to study consulting firms’ interventions, namely: the management approach, the consultocracy phenomenon and the phenomenon of elite hybridization. The phenomenon of elite hybridization reflects a promising heuristic approach.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".