Privacy Matters: Experiences of Rural and Remote Emergency Department Patients – A Mixed-Methods Research Conducted in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Objectives: This study aims to investigate patients' privacy experience when receiving care in emergency departments (EDs) in Newfoundland and Labrador, Canada. We aim to assess the level of satisfaction with privacy and to assess for factors that improve or worsen the privacy experience, not limited to patient demographics, length of stay, and hospital location. Methods: This study used a mixed-methods design, gathering quantitative and qualitative data using a telephone survey and semi-structured interviews. Our primary outcome measure was patients' privacy experience in the ED. The independent variables in our study were age, gender, ED location, patient-reported wait times, reason for ED visit, and healthcare provider involved in care. Results: Among the 821 patients who participated in the interviews, 1 in 4 patients (24%) did not have satisfactory ED privacy experiences. Multinominal logistic regression showed patients who waited 4+ hours before being examined by a provider [aOR = 0.34, 95% CI: 0.17-0.69] and those who visited the urban EDs [aOR = 0.17, 95% CI: 0.09-0.35] reported low levels of privacy. Furthermore, those whose overall length of stay was 4 to 8 hours [aOR = 0.44, 95% CI: 0.23-0.84] and 8+ hours [aOR = 0.36, 95% CI: 0.17-0.78] also reported dissatisfaction with ED privacy experience. Our qualitative analysis found privacy concerns in waiting rooms, triage areas, and curtain rooms, with females voicing more concerns than males. Conclusion: Patients with longer wait times and who have been seen in urban EDs experience less privacy. Our qualitative data shows that women also raised more privacy concerns than men and that waiting rooms and triage areas are the locations with the most reported privacy concerns. Patient experience and outcomes would benefit from improving patient privacy when receiving care in EDs.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".