Clients’ Perception of the Role of Transportation to the Hospital and Level of Hospital in Reducing Maternal Mortality in Calabar, Nigeria
Bibliographic record
Abstract
Transportation is vital in accessing healthcare services as well as reducing maternal mortality. This study examined clients’ perception of the role of transportation to the hospital and level of hospital in reducing maternal mortality in Calabar. This study was a cross-section descriptive design. Four (4) research questions were developed for the study. A total of 220 participants were recruited by proportionate sampling technique. Respondents were selected from four clinic days Tuesdays, Wednesdays, Thursdays and Fridays in each visit for a period of two weeks were used for the study. A structured questionnaire was used to collect data. The data were analysed using frequencies and percentages. The finding of the study revealed that: 153 (68%) agreed that good transportation increases the number of times participants go for an antenatal visit. 159 (72%) of the respondents viewed that good roads and vehicles make a journey to the hospital quick and easy. 177 (62%) opined that the best way to visit the hospital is by one’ s car or a taxi drop. 203 (93%) agreed that hospital has skilled midwives and doctors; 153 (70%) agreed that level of the hospital is reflective of low maternal and infant deaths; 159 (72%) agreed that healthcare team are highly skilled in handling both complicated and uncomplicated deliveries. 181 (78%) supports the notion that transportation plays a huge role in the reduction of maternal mortality in Calabar; 148 (67%) agreed that good road network has an indirect role in reducing maternal mortality in Calabar; 146 (67%) opined that controlled traffic helps reduced maternal mortality in Calabar, and 159 (71%) agreed that lack of access to transport for women in labour can increase stillbirth and maternal death rates. The study concludes that a good road network should be provided to reduce maternal mortality in Calabar.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".