Obligation towards medical errors disclosure at a tertiary care hospital in Dubai, UAE
Bibliographic record
Abstract
OBJECTIVE: The study aimed to identify healthcare providers' obligation towards medical errors disclosure as well as to study the association between the severity of the medical error and the intention to disclose the error to the patients and their families. DESIGN: A cross-sectional study design was followed to identify the magnitude of disclosure among healthcare providers in different departments at a randomly selected tertiary care hospital in Dubai. SETTING AND PARTICIPANTS: The total sample size accounted for 106 respondents. Data were collected using a questionnaire composed of two sections namely; demographic variables of the respondents and a section which included variables relevant to medical error disclosure. RESULTS: Statistical analysis yielded significant association between the obligation to disclose medical errors with male healthcare providers (X2 = 5.1), and being a physician (X2 = 19.3). Obligation towards medical errors disclosure was significantly associated with those healthcare providers who had not committed any medical errors during the past year (X2 = 9.8), and any type of medical error regardless the cause, extent of harm (X2 = 8.7). Variables included in the binary logistic regression model were; status (Exp β (Physician) = 0.39, 95% CI 0.16-0.97), gender (Exp β (Male) = 4.81, 95% CI 1.84-12.54), and medical errors during the last year (Exp β (None) = 2.11, 95% CI 0.6-2.3). CONCLUSION: Education and training of physicians about disclosure conversations needs to start as early as medical school. Like the training in other competencies required of physicians, education in communicating about medical errors could help reduce physicians' apprehension and make them more comfortable with disclosure conversations.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".