Knowledge and practice of handoff among doctors in a tertiary care hospital in Manipur: A cross-sectional study
Bibliographic record
Abstract
ABSTRACT Context: The exchange of patient information and the transfer of responsibility for patient care between health-care providers constitute a vital aspect of health-care communication and maintain continuum of care and patient safety. There are limited data on knowledge and practice of handoff among resident doctors in Manipur. Aims: To assess the knowledge and practice of handoff among resident doctors of a tertiary care hospital, in Imphal, and to determine the association between sociodemographic characteristics and handoff practice. Subjects and Methods: A cross-sectional study among 279 resident doctors of a tertiary care hospital in Manipur was conducted. The data were collected using a pretested structured questionnaire. Statistical Analysis: The data were analyzed using IBM SPSS version 26.0. Descriptive statistics such as percentage, mean, and standard deviation were used to represent quantitative data. Chi-square test or Fisher’s exact test were used for categorical data. P < 0.05 was considered statistically significant. Results: The mean age of participants was 29.65 ± 3.97 years. More than two-thirds of the participants were inadequately practicing handoff in patient care. There were significant associations between the knowledge pertaining to questions, “When does handoff occur in patient care?” ( P = 0.013), “What is the purpose of handoff?” ( P = 0.024), and “What are the characteristics of good handoff?”( P = 0.019) with the practice of handoff. Conclusion: Although more than 4/5 th of the doctors had good knowledge, only a quarter of them had adequate handoff practice.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".