A cross-sectional study on the knowledge and practice of medical certification of cause of death among junior doctors in a tertiary hospital in North-East India
Bibliographic record
Abstract
Background: Medical certification of cause of death (MCCD) is the formal document in which the doctor records the time, causes and circumstances of death of an individual. Inaccuracies and incomplete MCCD will lead to biased estimation of several epidemiological parameters. Hence this study was done to determine the knowledge and practice of MCCD among junior doctors and assess the association between knowledge and some selected variables of interest.Methods: A cross-sectional study was conducted among junior doctors constituting of interns, junior residents and post-graduates trainees of a tertiary hospital of Manipur from February to March 2020. A semi-structured, self-administered questionnaire was used. Data was entered in MS Excel and exported to SPSS version 21 where analysis was done. Descriptive statistics and Chi-square test was used for analysis and p<0.05 was taken as significant. Ethical clearance was obtained from the Institutional ethics committee.Results: Out of the 334 total respondents females constituted 53%. Only 88(26.3%) had satisfactory knowledge, and only 14% (47) of the respondents had ever issued MCCD.No significant association was seen between knowledge score and current department of posting, current designation, gender, religion and work experience.Conclusions: Only a quarter of the respondents (26.3%) were having satisfactory knowledge. There is a need to organize frequent workshops, seminars and induction training highlighting the importance of MCCD for the junior doctors with regular audits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".