Health and Nutrition Promotion Programs in Papua New Guinea: A Scoping Review
Bibliographic record
Abstract
There is a rising prevalence of non-communicable diseases (NCDs) in Papua New Guinea (PNG), adding to the disease burden from communicable infectious diseases and thus increasing the burden on the healthcare system in a low-resource setting. The aim of this review was to identify health and nutrition promotion programs conducted in PNG and the enablers and barriers to these programs. Four electronic databases and grey literature were searched. Two reviewers completed screening and data extraction. This review included 23 papers evaluating 22 health and nutrition promotion programs, which focused on the Ottawa Charter action areas of developing personal skills (12 programs), reorienting health services (12 programs) and strengthening community action (6 programs). Nineteen programs targeted communicable diseases; two addressed NCDs, and one addressed health services. Enablers of health promotion programs in PNG included community involvement, cultural appropriateness, strong leadership, and the use of mobile health technologies for the decentralisation of health services. Barriers included limited resources and funding and a lack of central leadership to drive ongoing implementation. There is an urgent need for health and nutrition promotion programs targeting NCDs and their modifiable risk factors, as well as longitudinal study designs for the evaluation of long-term impact and program sustainability.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".