AN EXPLORATORY STUDY OF REGISTERED NURSES’ EXPERIENCES IN PATIENT INFORMATION PRIVACY AND SECURITY WITHIN THE PROVINCES OF ALBERTA(AB) AND SASKATCHEWAN(SK)
Bibliographic record
Abstract
The purpose of this qualitative research was to gain a better understanding of the experiences of registered nurses in patient information privacy and security in Alberta (AB) and Saskatchewan (SK) health regions. Studies of this nature are rarely if ever conducted as topics like ethics, breaches, and self-reflection of our own professional practices are sensitive in nature to all health care professionals. Exploring patient information security/privacy falls into this delicate and complex category. As an outsider to the nursing profession/discipline,I had the privilege of conducting this study.Surprisingly, twenty nurses from the medical/surgical/critical care specialties did agree to participate in this study. Interpretive Description (ID) was the methodology chosen for this study. Face to face interviews were conducted with twelve nurses from large and small cities in each of two neighboring Prairie provinces in Canada. Nine nurses from AB and eleven nurses from SK shared their experiences of compliance to their regulatory health information Acts in each province: The Alberta Health Information Act (HIA), and the Saskatchewan Health Information Protection Act (HIPA). Unexpectedly, new definitions of what constitutes patient information privacy and security, and what comprises a breach of patient information occurring was interpreted from the data.These new key definitions were interpreted from the described experiences of the nurses themselves, as the trusted protector of patient information. Comparisons were made between the two provinces on the perceptions/experiences of nurses with regard to the security and privacy of electronic records compared to paper records.A trusted relationship builds between the nurse and the patient with regard to patient or health information. Family relationships were to be among the most challenging. Breaches were found to occur intentionally or unintentionally.Findings and recommendations from this study will add to the knowledge-base of nursing and health care professional practice, ethics and informatics. The findings could also positively influence the personal attitudes of nurses towards patient information privacy
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".