Challenges and opportunities of human resource management activities for neglected tropical diseases in Liberia
Bibliographic record
Abstract
People affected by skin neglected tropical diseases (NTDs) are best cared for by a motivated, well-directed, competent and well-resourced health workforce. There is limited evidence about performance management for health workforce relating to NTD tasks. We explored human resource management relating to skin NTDs, with a focus on performance management. We carried out qualitative and participatory research with health workers across health systems levels in Liberia to explore experiences of caring for people with skin NTDs and views on optimal human resource management (HRM) practices. We conducted key informant interviews with national health systems policymakers (16) and county health workers (32); in-depth interviews with health workers (36); focus group discussions with health workers (4); and photovoice with 15 community health assistants and community health promoters, purposively selected for maximum variation. All interviews and FGDs were transcribed and analysed using thematic framework approach. We found health workers often have strong intrinsic motivation to care for people affected by skin NTDs. However, this is undermined by weak HRM structures particularly in geographic areas where integrated services for NTDs requiring case management have not yet rolled out. The main challenges described include: limited awareness of NTD-related roles, and mental health support provision role, particularly at facility level, gaps in knowledge and skills (how to identify, diagnose and manage skin NTDs), irregular supervision and limited resources to deliver care. Our findings have informed collaborative development of a bundle of HRM approaches to strengthen performance of health workers caring for patients with skin NTDs, including participatory training informed by adult learning-based approaches, supportive supervision, provision of job tasks, NTD manual and related tools, essential resource provision for community health assistants and promoters (CHAs and CHPs) and non-cash awards.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".