Quality of Pre-Service Midwifery Education in Public and Private Midwifery Schools in Afghanistan: A Cross Sectional Survey
Bibliographic record
Abstract
Abstract Background Midwives are the key skilled birth attendants in Afghanistan. Rapid assessment of public and private midwifery education schools was conducted in 2017 to examine compliance with national educational standards. Aim was to assess midwifery education to inform Afghanistan Nurses and Midwives Council and other stakeholders priorities for improving quality of midwifery education. Methods A cross-sectional assessment was conducted from September 12–December 17, 2017, using a modified Midwifery Education Rapid Assessment Tool to assess education quality aspects related to infrastructure, management, teachers, preceptors, clinical practice sites, curriculum and students in 29 midwifery schools. A purposive sample of six Institute of Health Sciences schools, seven Community Midwifery Education schools and 16 private midwifery schools was used. Participants were midwifery school staff, students and clinical preceptors. Results Libraries were available in 28/29 (97%) schools, active skills labs in 20/29 (69%), childbirth simulators in 17/29 (59%) and newborn resuscitation models in 28/29 (97%). School managers were midwives in 21/29 (72%) schools. Median numbers of students per teacher and students per preceptor were 8 (range 2–50) and 6 (range 2–20). There were insufficient numbers of teachers practicing midwifery (132/163; 81%), trained in teaching skills (113/163; 69%) and trained in emergency obstetric and newborn care (88/163; 54%). There was an average of 13 students at clinical sites in each shift. Students managed an average of 15 births independently during their training, while 40 births are required. Twenty-four percent (7/29) of schools used the national 2015 curriculum alone or combined with an older one. Ninety-one percent (633/697) of students reported access to clinical sites and skills labs. Students mentioned, however, insufficient clinical practice, lack of education materials, transport facilities and disrespect from school teachers, preceptors and clinical site providers as challenges. Conclusions Positive findings included availability of required infrastructure, amenities, approved curricula in 7 of the 29 midwifery schools, appropriate clinical sites and students’ commitment to work as midwives upon graduation. Gaps identified were use of different often outdated curricula, inadequate clinical practice, underqualified teachers and preceptors and failure to graduate all students with sufficient skills such as independently having supported 40 births.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".