Bibliographic record
Abstract
Locked filing cabinets are no longer enough to ensure security of research data and results.In the 21 st century, cybersecurity is foundational to the ethical conduct of research and its application to health services and policy.It matters for ensuring the confidentiality of personal data, for integrity of research systems, for safety of digital interventions that are being studied, for protection of intellectual property, and more.The challenge is real, not theoretical.The National Research Council has experienced state-sponsored cyberattacks (Moens et al. 2015).Universities have reported ransomware attacks (CBC News 2016).And cyberattacks are relatively frequent in the health sector, a potential source of vulnerability that is recognized by health sector leaders and citizens alike (Zelmer 2018).For instance, multiple organizations have reported malware, spyware or ransomware attacks; phishing and cyber fraud; denial of service attacks; and human error that affected critical systems.On a global scale, the World Medical Assembly has stated that "cyber-attacks on healthcare systems and other critical infrastructure represent a cross-border issue and a threat to public health" (WMA 2016).Addressing these challenges depends on both individual and collective action.At a recent national Summit, health leaders and cyber experts explored options for strengthening the health sector' s resilience to cyber threats (HealthcareCAN 2018a).Building on the National Strategy for Critical Infrastructure endorsed by federal, provincial and territorial governments, participants declared a shared commitment to cybersecurity and to six tangible actions to increase preparedness:
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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.025 | 0.040 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".