Health Facility Factors Influencing the Implementation of PTBI during the Provision of Intrapartum and Perinatal Care in Embu County
Bibliographic record
Abstract
The objective of the study was to establish the health facility factors (HFF) influencing the implementation of PTBI during the provision of intrapartum and perinatal care in Embu County The study used a cross-sectional design. Random sampling technique was used to determine the sample size of 94 HCP, while the Purposive sampling technique was used to sample 24 client files (a quarter of the sample size) and 5 Key informants. The study was conducted in three hospitals in Embu County, Kenya. Questionnaires and document review guides were used to collect quantitative data and Key informant interview (KII) guides were used to collect qualitative data. Data analysis was done using SPSS version 21, descriptive statistics; Chi squires, Fisher’s test, and binary logistic model. Qualitative data were categorized into themes. Data findings were presented using tables and charts. Results: The findings in this study revealed that the majority of HCP in the maternity unit who agreed with the statement that adequacy of HFF influences implementation of PTBI were associated with the low implementation of PTBI as compared to those who disagreed. On the other hand, those HCP who agreed that there was adequate HFF were also associated with the low implementation of PTBI as compared to those HCP who disagreed. The findings further revealed there were inadequate staff, transport, and finances while drugs and equipment were adequate. The former three are important aspects in the implementation of PTBI and their inadequacy may lead to the low implementation of PTBI. In addition, this study also revealed that the highest number of respondents reported HFF affects the implementation of PTBI to a large extent as compared to those who reported moderately and low extent respectively. Respondents’ responses were echoed by the 5 KIIs of whom, four reported that the level of implementation is affected by HFF to a moderate and large extent respectively. In addition, the study recommends the county government consider improving HFF by; providing facilities for KMC, improving transport facilities, recruiting more skilled staff, and increasing funding in the field of midwifery/reproductive health to enhance the implementation of PTBI. Adequate funding will enhance staff recruitment, maintenance of transport, and timely procurement of drugs and equipment. Consequently, promoting the implementation of PTBI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".