The Practical Approach to Care Kit (PACK) guide: developing a clinical decision support tool to simplify, standardise and strengthen primary healthcare delivery
Bibliographic record
Abstract
For the primary health worker in a low/middle-income country (LMIC) setting, delivering quality primary care is challenging. This is often complicated by clinical guidance that is out of date, inconsistent and informed by evidence from high-income countries that ignores LMIC resource constraints and burden of disease. The Knowledge Translation Unit (KTU) of the University of Cape Town Lung Institute has developed, implemented and evaluated a health systems intervention in South Africa, and localised it to Botswana, Nigeria, Ethiopia and Brazil, that simplifies and standardises the care delivered by primary health workers while strengthening the system in which they work. At the core of this intervention, called Practical Approach to Care Kit (PACK), is a clinical decision support tool, the PACK guide. This paper describes the development of the guide over an 18-year period and explains the design features that have addressed what the patient, the clinician and the health system need from clinical guidance, and have made it, in the words of a South African primary care nurse, 'A tool for every day for every patient'. It describes the lessons learnt during the development process that the KTU now applies to further development, maintenance and in-country localisation of the guide: develop clinical decision support in context first, involve local stakeholders in all stages, leverage others' evidence databases to remain up to date and ensure content development, updating and localisation articulate with implementation.
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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 | 0.030 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.021 |
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".