Health Care Workers’ Experiences, Challenges of Obstetric Referral Processes and Self-Reported Solutions in South Western Uganda: Mixed Methods Study
Bibliographic record
Abstract
Introduction: In resource limited settings, the highest burden of adverse maternal-fetal outcomes at referral hospitals is registered from emergency obstetric referrals from lower health facilities. Implementation of referral protocols has not been optimally successful possibly attributed to lack of understanding of profile of obstetric referrals and local challenges faced during implementation process. Objective: This study described the profile of emergency obstetric referrals, challenges faced in implementation of obstetric referral processes and explored self-reported solutions by health workers. Methods: This was a mixed methods study done at Mbarara Regional Referral Hospital (MRRH) and health centre IVs in South-Western Uganda. We consecutively recruited emergency obstetric referrals from Isingiro district for delivery at MRRH. Using a pre-tested questionnaire, we collected demographics, obstetric and referral characteristics. We described the profile of referrals using frequencies and proportions based on demographics, obstetric and referral characteristics. We conducted focus group discussions and in-depth interviews with health workers using discussion/interview guides. Using thematic analysis, we ascertained the challenges and health worker self-reported solutions. Results: We recruited 161 referrals: 104(65%) were below 26 years, 16(10%) had no formal education, 11(7%) reported no income, 151(94%) had no professional-escort, 137(85%) used taxis, 151(96%) were referred by midwives. Common diagnoses were previous cesarean scar (24% [n=39]) and prolonged labour (21% [n=33]). There was no communication prior to referral and no feedback from MRRH to lower health facilities. Other challenges included inconsistencies of ambulance and anesthesia services, electric power, medical supplies, support supervision, and harassment by colleagues. Self-reported solutions included the use of phone call technology for communication, audit meetings, support supervision and increasing staffing level. Conclusion: Most referrals are of poor social-economic status, use taxis, and lack professional-escort. Health workers suffer harassment, lack of communication and shortage of supplies. We need to experiment whether mobile phone technology could solve the communication gap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".