From public health to cyber hygiene: Cybersecurity and Canada’s healthcare sector
Bibliographic record
Abstract
The COVID-19 pandemic has ushered in a wave of cyberattacks targeting the healthcare sector, including against hospitals, doctors, patients, medical companies, supply chains, universities, research laboratories, and public health organizations at different levels of jurisdiction and across the public and private sectors. Despite these concerns, cybersecurity in Canadian healthcare is significantly understudied. This article uses a series of illustrative examples to highlight the challenges, outcomes, and solutions Canada might consider in addressing healthcare cybersecurity. The article explores the various rationales by which Canadian healthcare may be targeted, unpacks several prominent types of cyberattack used against the healthcare sector, identifies the different malicious actors motivated to conduct such attacks, provides insights derived from three empirical cases of healthcare cyberattack (Boston Children’s Hospital [2014], Anthem [2015], National Health Service [2017]), and concludes with lessons for a Canadian response to healthcare cybersecurity from several international perspectives (e.g., Australia, New Zealand, the UK, Norway, and the Netherlands).
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".