Human factors in cybersecurity: Designing an effective cybersecurity education program for healthcare staff
Bibliographic record
Abstract
Leaders who promote cybersecurity education focused on the human factors of cyberattack build a resilient workforce that complements technical protections, reducing organizational risk. Cybersecurity is a priority for information technology teams, relying primarily on technology to protect systems. As technical protections mature, the vulnerability shifts to human factors. Education must focus on the risk presented by humans rather than machines. A human factors-centred education program trains human reaction to threats considering the unique healthcare environment. Leaders may look to industries, like aviation, experiencing similar technical advancement, for education practices based on human factors. This article outlines a cybersecurity education program developed for healthcare, applying strategies adopted from commercial aviation. Four core pillars of training are defined: (1) dynamic education delivery options, (2) social engineering focused simulations, (3) high-risk positions and role-based training, and (4) stakeholder and leadership engagement. The first phase of implementation has been analyzed and offers some lessons for health leaders.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".