Supporting middle-cadre health care workers in Malawi: lessons learned during implementation of the PALM PLUS package
Bibliographic record
Abstract
BACKGROUND: The government of Malawi is committed to the broad rollout of antiretroviral treatment in Malawi in the public health sector; however one of the primary challenges has been the shortage of trained health care workers. The Practical Approach to Lung Health Plus HIV/AIDS in Malawi (PALM PLUS) package is an innovative guideline and training intervention that supports primary care middle-cadre health care workers to provide front-line integrated primary care. The purpose of this paper is to describe the lessons learned in implementing the PALM PLUS package. METHODS: A clinical tool, based on algorithm- and symptom-based guidelines was adapted to the Malawian context. An accompanying training program based on educational outreach principles was developed and a cascade training approach was used for implementation of the PALM PLUS package in 30 health centres, targeting clinical officers, medical assistants, and nurses. Lessons learned were identified during program implementation through engagement with collaborating partners and program participants and review of program evaluation findings. RESULTS: Key lessons learned for successful program implementation of the PALM PLUS package include the importance of building networks for peer-based support, ensuring adequate training capacity, making linkages with continuing professional development accreditation and providing modest in-service training budgets. The main limiting factors to implementation were turnover of staff and desire for financial training allowances. CONCLUSIONS: The PALM PLUS approach is a potential model for supporting mid-level health care workers to provide front-line integrated primary care in low and middle income countries, and may be useful for future task-shifting initiatives.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".