Five lessons from a mid-level health manager intervention to increase uptake of tuberculosis prevention therapy in Uganda: ‘it is a completely different thing to implement what you know.’
Bibliographic record
Abstract
BACKGROUND: Leadership skills are essential for middle-level healthcare manager efficacy. Capacity-building efforts may attempt behavioural change by filling 'knowledge gaps' while neglecting a sustainable application of that knowledge. Sustainable application of that knowledge, or implementation know-how, must resonate with local cultural patterns. When it is neglected, root issues like unclear decision-making space and local authority to interpret policy during implementation remain unaddressed. Particularly in decentralized healthcare systems, the impact can appear in implementation challenges, subjective decision-making, poor teamwork, and an absence of disseminating best practices. OBJECTIVES: The SEARCH-IPT trial led a series of mini-collaborative meetings, which provided business leadership and management training for an intervention group of mid-level healthcare system managers in rural Eastern, East-Central, and Southwestern Uganda to see whether this would increase uptake of isoniazid-prevention therapy (IPT) for people living with HIV (PLHIV) in intervention districts. IPT is known to reduce active tuberculosis (TB), a leading cause of death among PLHIV, by 40-60%. METHODS: We performed a thematic analysis of six focus-group discussions from this intervention (held in May 2019, January 2020, September 2021) and 23 key informant interviews with control group participants (between February and August 2019 and September and December 2020). RESULTS: Analysis revealed five implementation skill sets District Health Officers (DHOs) and District Tuberculosis and Leprosy Supervisors (DTLSs) deployed to achieve sustainable implementation and realize their decision-making space. The five practices were as follows: data-based decision-making, root-cause analysis, quality assurance, evidence-based empowerment, and sharing best practices with colleagues. CONCLUSION: These practices reached beyond outcome measures to address root problems around the DHO's range of authority and elicit buy-in from district health workers. For successful capacity building at the mid-manager level, focusing on core practices as part of competency is objectively implementable and measurable at the system level and does not rely on DHO self-assessments.
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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".