Assessment of the non-communicable diseases kit for humanitarian emergencies in Yemen and Libya
Bibliographic record
Abstract
Emergency health kits are a vital way of providing essential medicines and supplies to health clinics during humanitarian crises. The WHO non-communicable diseases (NDCs) kit was developed 5 years ago, recognising the increasing challenge of providing continuity of care and secondary prevention of NCDs and exacerbations, in such settings. Monitoring and evaluation of emergency health kits is an important process to ensure the contents are fit for purpose and to assess usability and utility. However, there are also challenges and limitations in collecting the relevant data to do so.This Practice paper provides a summary of the key methodologies, findings and limitations of NCD kit assessments conducted in Libya and Yemen. Methodologies included a combination of semistructured interviews, surveys with healthcare workers, NCD knowledge tests and quantifying the remaining contents.The kit was able to support the vital delivery of NCD patient care in some complex humanitarian settings and was appreciated by health facilities. However, there were also some challenges affecting kit use. Some kit contents were found to be in greater or lesser quantities than required, and medicine brands and country of origin affected acceptability. Supply chains were affected by the humanitarian situations, with some kits being held up for months prior to arrival. Furthermore, healthcare staff had received limited NCD training and were unable to dispense certain medicines, such as psychotropics, at the primary care level. Further granularity of kit modules, predeployment facility assessments, increased NCD training opportunities and a monitoring system could improve the utility of the kits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".