<p>Health care professionals&rsquo; knowledge and awareness of the ICD-10 coding system for assigning the cause of perinatal deaths in Jordanian hospitals</p>
Bibliographic record
Abstract
OBJECTIVES: There is a lack of studying vital registration and disease classification systems in low- and middle-income countries. This study aimed to assess health care professionals' (HCPs') level of awareness, knowledge, use, and perceived barriers of the International Classification of Diseases, 10th version (ICD-10) as well as their perceptions of the electronic neonatal death registration system. PARTICIPANTS AND METHODS: A mixed method approach including descriptive cross-sectional quantitative and focus groups with HCPs (physicians, nurses, and midwives) was used to collect data from four major selected hospitals in Jordan. A total of 16 focus groups were conducted. Also, a survey, which included three case studies about the ability of nurses and physicians to identify cause of death, was completed using structured face-to-face interviews. RESULTS: Overall, there was congruency between both the quantitative results and the qualitative findings. The majority of nurses and physicians in the four hospitals were not familiar with the ICD-10 coding system and hence reported minimal use of the coding system. Additionally, the majority of HCPs were not aware whether or not their departments used the ICD-10 to record perinatal mortality. These HCPs identified that lack of knowledge, time, staff and support, and an effective and comprehensive electronic system that allows physicians to accurately choose the exact cause of death were their main barriers to the use of the ICD-10 coding system. CONCLUSION: Our findings emphasize the importance of developing an effective and comprehensive electronic system which allows HCPs to accurately report and register all perinatal deaths. This system needs to account for the direct and indirect causes of death and for contributing factors such as maternal conditions at the time of perinatal death. Training HCPs on how to use the system is vital for the success and accuracy of the data registration process.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".