Knowledge, Attitude, and Perception of Postmortem Examination Among Doctors and Nurses in a Tertiary Hospital of Sokoto, Nigeria
Bibliographic record
Abstract
Postmortem examination is a highly specialized surgical procedure that consists of a thorough examination of a corpse to determine the cause and manner of death and to evaluate any disease or injury that may be present. This study aimed to assess the knowledge, attitude, and perception of postmortem examination among doctors and nurses in a tertiary health care of Sokoto state. A cross-sectional study design was used, and a total of 149 doctors and nurses participated in the study. Respondents were recruited into the study using probability proportionate to size followed by a simple random sampling method. Data were obtained through self-administered questionnaires, and the data were analyzed using Statistical Package for Social Sciences Version 17.0. Descriptive statistics, Chi-square test, and multinomial logistic regression analysis were carried out. The mean age of respondents was 31.6 (5.6) years. There were more nurses than doctors (60.4% vs. 39.6%) in the study. More than three-quarter (80%) of the respondents had fair to good knowledge of postmortem examination. While many respondents expressed positive attitudes and perceptions, less than half were willing to accept organs from deceased donors. Respondents' profession influenced both the knowledge (P > 0.001, odds ratio [OR] = 13.95) and attitude (P < 0.04, OR = 2.49) to postmortem examination. Although greater than three-quarter of respondents had fair to good knowledge and many expressed positive attitudes and perceptions with respect to postmortem examination, there is need to create more awareness on medical benefit of postmortem examination.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".