Survey of the capacity for essential surgery and anaesthesia services in Papua New Guinea
Bibliographic record
Abstract
OBJECTIVE: To assess capacity to provide essential surgical services including emergency, obstetric and anaesthesia care in Papua New Guinea (PNG) in order to support planning for relevant post-2015 sustainable development goals for PNG. DESIGN: Cross-sectional survey. SETTING: Hospitals and health facilities in PNG. PARTICIPANTS: 21 facilities including 3 national/provincial hospitals, 11 district/rural hospitals, and 7 health centres. OUTCOME MEASURES: The WHO Situational Analysis Tool to Assess Emergency and Essential Surgical Care (WHO-SAT) was used to measure each participating facility's capacity to deliver essential surgery and anaesthesia services, including 108 items related to relevant infrastructure, human resources, interventions and equipment. RESULTS: While major surgical procedures were provided at each hospital, fewer than 30% had uninterrupted access to oxygen, and 57% had uninterrupted access to resuscitation bag and mask. Most hospitals reported capacity to provide general anaesthesia, though few hospitals reported having at least one certified surgeon, obstetrician and anaesthesiologist. Access to anaesthetic machines, pulse oximetry and blood bank was severely limited. Many non-hospital health centres providing basic surgical procedures, but almost none had uninterrupted access to electricity, running water, oxygen and basic supplies for resuscitation, airway management and obstetric services. CONCLUSIONS: Capacity for essential surgery and anaesthesia services is severely limited in PNG due to shortfalls in physical infrastructure, human resources, and basic equipment and supplies. Achieving post-2015 sustainable development goals, including universal healthcare, will require significant investment in surgery and anaesthesia capacity in PNG.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".