Surgical Skill, Providers and Infrastructure Needs Assessment in North Gondar, Ethiopia: A Mixed Method Study
Bibliographic record
Abstract
Background: The shortage of skilled surgical providers in Sub-Saharan Africa is reaching a crisis level.The Canadian Network for International Surgery has been delivering structured surgical skills courses in Ethiopia for 15 years. However, an assessment of met needs, and ongoing barriers to surgical care has never been done. Ethiopia has set out plans to expand surgical capacity at the district hospital levels through upgrading and building hospitals, and task shifting through a surgical health officer program. This study aimed to assess the met need for surgical infrastructure, providers, and educationin in North Gondar Zone, Ethiopia. Sub-objectives are to assess the perceived values of a structured surgical training courses, and to identify ongoing barriers to emergency surgery. Method: This mixed-method of study employed: semi-structured interviews to surgical providers, a review of operative records, an infrastructure needs assessment. The research also used questionnaires which was distributed to medical trainees to assess the met needs, and to identify barriers to care.A total of190 trainees participated in the survey. In addition, 12 participants were involved in the interview from 4 hospitals. I n addition, descriptive statistics were used to describe the study subjects and the surgical skill needs using tables and graphs. Result: Emergency surgery was only performed in Gondar University Hospital with a met need for a cesarean section of only 15%. There was a severe shortage of both hospitals, and care providers in the zone. Lack of consumable emergency equipment was cited as the greatest barrier to delivering emergency care at the district level. Conclusion: Shortage of providers, inadequate surgical infrastructure, and a severe lack of continuing skill improvement needs were observed.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".