Reducing Security Incidents in a Canadian PHIPA Regulated Environment with an Employee-Based Risk Management Strategy
Bibliographic record
Abstract
The paper uses a case study research approach in defining how an employee based risk management strategy such as employee information security training, employee motivation, and quality assurance can be used to reduce security incidents in a Canadian PHIPA regulated environment. During the research, information security professionals and employees were asked direct questions aimed at understanding the reasons why internal data breaches are recurrent, and what are users’ perception and understanding of existing security policies, processes, and their role in protecting information in their work environment. By using a qualitative case study research design method, data was collect from a small but targeted group of information security professionals and employees within healthcare organization in Ontario. The gathered data was analyzed to identify what are the main causes of security incidents, and what organizations,in the healthcare field can do to better involve their employees for the reduction of breaches and incidents. \nThe recommendations made by this research paper have the potential of influencing an organization’s \norganizational culture and employee behavior. The main goal of this paper was to develop an employee based risk management strategy for enterprise level risk management focused on positively influencing employee behaviour.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".