Knowledge, Attitudes and Practice of Healthcare Ethics and Law Among Doctors and Nurses in Barbados
Bibliographic record
Abstract
Background: The aim of the study is to assess the knowledge, attitudes and practices among healthcare professionals in Barbados in relation to healthcare ethics and law in an attempt to assist in guiding their professional conduct and aid in curriculum development. Methods: A self-administered structured questionnaire about knowledge of healthcare ethics, law and the role of an Ethics Committee in the healthcare system was devised, tested and distributed to all levels of staff at the Queen Elizabeth Hospital in Barbados (a tertiary care teaching hospital) during April and May 2003. Results: The paper analyses 159 responses from doctors and nurses comprising junior doctors, consultants, staff nurses and sisters-in-charge. The frequency with which the respondents encountered ethical or legal problems varied widely from 'daily' to 'yearly'. 52% of senior medical staff and 20% of senior nursing staff knew little of the law pertinent to their work. 11% of the doctors did not know the contents of the Hippocratic Oath whilst a quarter of nurses did not know the Nurses Code. Nuremberg Code and Helsinki Code were known only to a few individuals. 29% of doctors and 37% of nurses had no knowledge of an existing hospital ethics committee. Physicians had a stronger opinion than nurses regarding practice of ethics such as adherence to patients' wishes, confidentiality, paternalism, consent for procedures and treating violent/non-compliant patients (p = 0.01) Conclusion: The study highlights the need to identify professionals in the workforce who appear to be indifferent to ethical and legal issues, to devise means to sensitize them to these issues and appropriately training them.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".