Crossing borders: the PACK experience of spreading a complex health system intervention across low-income and middle-income countries
Bibliographic record
Abstract
Developing a health system intervention that helps to improve primary care in a low-income and middle-income country (LMIC) is a considerable challenge; finding ways to spread that intervention to other LMICs is another. The Practical Approach to Care Kit (PACK) programme is a complex health system intervention that has been developed and adopted as policy in South Africa to improve and standardise primary care delivery. We have successfully spread PACK to several other LMICs, including Botswana, Brazil, Nigeria and Ethiopia. This paper describes our experiences of localising and implementing PACK in these countries, and our evolving mentorship model of localisation that entails our unit providing mentorship support to an in-country team to ensure that the programme is tailored to local resource constraints, burden of disease and on-the-ground realities. The iterative nature of the model's development meant that with each country experience, we could refine both the mentorship package and the programme itself with lessons from one country applied to the next-a 'learning health system' with global reach. While not yet formally evaluated, we appear to have created a feasible model for taking our health system intervention across more borders.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".