Building leadership and managerial capacity for maternal and newborn health services
Bibliographic record
Abstract
BACKGROUND: Strengthening leadership and management is important for building an effective and efficient health system. This paper presents the findings from a L&M capacity building initiative which was implemented as part of a larger study aimed at improving maternal and newborn outcomes within primary health facilities in the Morogoro, Tanzania. METHODS: The initiative, involving 30 stakeholders from 20 primary health facilities, 4 council health management teams and the regional health management team in the Morogoro region, provided leadership and managerial training through two 5-day in-person workshops, onsite mentoring, and e-learning modules. The initiative was evaluated using a pre-post design. Quantitative instruments included the 'Big Results Now' star-rating assessments and a team-developed survey for health providers/managers. The 'Big Results Now' star-rating assessments, conducted in 2018 (19 facilities) and 2021 (20 facilities), measured overall facility leadership and management capability, with comparisons of star-ratings from the two time-points providing indication of improvement. The survey was used to measure 3 key leadership indicators - team climate, role clarity/conflict and job satisfaction. The survey was completed by 97 respondents at baseline and 100 at follow up. Paired t-tests were used to examine mean score differences for each indicator. Triangulated findings from focus groups with 99 health providers and health management team members provided support and context for quantitative findings. RESULTS: Star-ratings increased in 15 (79%) of 19 facilities, with the number of facilities achieving the target of 3 plus stars increasing from 2 (10%) in 2018 to 10 (50%) in 2021, indicating improved organizational performance. From the survey, team climate, job satisfaction and role clarity improved across the facilities over the 3 project years. Focus group discussions related this improvement to the leadership and managerial capacity-building. CONCLUSION: Improved leadership and managerial capacity in the participating health facilities and enhanced communication between the health facility, council and regional health management teams created a more supportive workplace environment, leading to enhanced teamwork, job satisfaction, productivity, and improved services for mothers and newborns. Leadership and managerial training at all levels is important for ensuring efficient and effective health service provision.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".