A review of the role of digitalization in health risk management in extractive industries – a study motivated by COVID-19
Bibliographic record
Abstract
Purpose While disruptions as a result of the COVID-19 pandemic resulted in the failure of some companies, others embraced innovative digital technologies to face the challenge posed by COVID-19. The COVID-19 crisis is also an opportunity for the extractive industry (EI) sectors to review their digitalization processes. The purpose of this paper is to conduct a systematic review of infectious disease mitigation in EI and to evaluate the resilience of these industries as they address pandemic prevention and control. Design/methodology/approach Multi-case studies including digital and organizational responses to COVID-19 were analyzed to evaluate the readiness of health risk management (HRM) and resilience of EIs against the pandemic. The evaluation uses Google Scholar and Trends searches to compare the level of relevant activity in EIs with other industries. Findings Although EI sectors have various plans for minimizing pandemic impacts, unexpected disruptions and delays of the COVID-19 responses revealed many limitations of the existing HRM system. Digital technologies (e.g. artificial intelligence-based public health monitoring, digital collaboration, wearable health tracking and 3D printing) demonstrated their remarkable benefits in the pandemic responses and nontechnical elements affecting technology adoption (TA). Originality/value Lessons learned from the deployment of digital technologies against the pandemic help to improve the organizational capacity to deal effectively with future outbreaks and suggest lessons for the future trajectory of TA in these industries.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".