Level of Partograph completion and healthcare workers’ perspectives on its use in Mulago National Referral and teaching hospital, Kampala, Uganda
Bibliographic record
Abstract
BACKGROUND: The appropriate use of the Partograph allows early identification of labour related complications and prevents deaths. We, therefore, sought to determine the level of Partograph completion and healthcare worker perspectives towards its utilization. METHODS: This study had two components; a hospital-based cross-sectional descriptive chart review at the Mulago National Referral Hospital, Kampala, Uganda and a qualitative study involving four Focus Group Discussions (FGDs) with ward nurses, midwives and postgraduate residents. Data from the FGDs were analyzed using thematic -content analysis in Open Code software. The quantitative data were summarized using descriptive statistical analysis, means and proportions. RESULTS: Among the 355 Partographs reviewed, 79.1% had incomplete documentation of age, 52.7% gravidity, and 3.2% parity. In about 61%, the specific parameters for fetal monitoring, maternal monitoring and labour progress were incomplete. From the FGDs, the healthcare workers reported being unable to complete the Partographs due to the overwhelming numbers of expectant mothers and other staff responsibilities. Congestion in the maternity ward reduced the Partograph completion rates. The availability of other monitoring tools, limitation in skills, inadequate equipment and supplies, and the state of the mother at the presentation to the hospital all made Partograph use and completion challenging. CONCLUSIONS: The majority of Partographs started by health workers were incomplete. The time required to document, health system challenges, status of mother at presentation, and the high workload undermined completion of the Partograph at this high volume facility.
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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 | 0.006 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".