Strengthening and Institutionalizing the Leadership and Management Role of Frontline Nurses to Advance Universal Health Coverage in Zambia
Bibliographic record
Abstract
In Zambia, nurses and nurse-midwives lead more than half of rural facilities and guide primary health care delivery. Based on a formative assessment, the Ministry of Health (MOH) determined that improved leadership capacity and management skills of facility heads would help maximize the potential of Zambia's community-level investments. In support of these efforts, the Primary Health Care to Communities (PHC2C) initiative designed and tested a 12-month blended learning program for a certificate in leadership and management practice (CLMP) to build leadership and management competencies of rural facility heads, including increasing their ability to lead frontline teams and strengthening their skills and confidence in technology use. The CLMP was created with leadership from the MOH, technical guidance from the University of Zambia, and expertise from PHC2C partners IntraHealth International, Johnson & Johnson, and mPowering Frontline Health Workers. In total, 20 nurse facility heads and 5 district nurse supervisors in 20 rural facilities across 5 districts were selected to test the course content and delivery approach. A mixed-methods approach, including evaluation of facility heads' presentations on community health improvement projects, focus group discussions with community members, and key informant interviews with nurses, clinical officers, and other stakeholders, was used to assess the results. Findings suggested that the facility heads had successfully strengthened their leadership and management competencies, increased their ability to lead frontline teams, and strengthened their skills and confidence in use of technology, including using a WhatsApp community of practice for support and consultation with other colleagues, with demonstrated improvements in the quality and accessibility of services. Based on assessment results and lessons from the test intervention, the Zambian government has committed to institutionalize CLMP as a national continuing professional development program, required for nurses posted to lead rural facilities. The planning, design, and implementation of this program offer an example to other countries and global actors of how nurses empowered with competence and confidence can play a significant role in coordinating the maze of community actors and navigating the complexities of community health systems to advance primary health care and universal health coverage.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".