Conceptualizing and implementing a health workforce registry in Nigeria
Bibliographic record
Abstract
BACKGROUND: Nigeria's health sector aims to ensure that the right number of health workers that are qualified, skilled, and distributed equitably, are available for quality health service provision at all levels. Achieving this requires accurate and timely health workforce information. This informed the development of the Nigeria Health Workforce Registry (NHWR) based on the global, regional, and national strategies for strengthening the HRH towards achieving universal health coverage. This case study describes the process of conceptualizing and establishing the NHWR, and discusses the strategies for developing sustainable and scalable health workforce registries. CASE PRESENTATION: In designing the NHWR, a review of existing national HRH policies and guidelines, as well as reports of previous endeavors was done to learn what had been done previously and obtain the views of stakeholders on how to develop a scalable and sustainable registry. The findings indicated the need to review the architecture of the registry to align with other health information systems, develop a standardized data set and guidance documents for the registry including a standard operating procedure to ensure that a holistic process is adopted in data collection, management and use nationally. Learning from the findings, a conceptual framework was developed, a registry managed centrally by the Federal Ministry of Health was developed and decentralized, a standardized tool based on a national minimum data was developed and adopted nationally, a registry prototype was developed using iHRIS Manage and the registry governance functions were integrated into the health information system governance structures. To sustain the functionality of the NHWR, the handbook of the NHWR that comprised of an implementation guide, the standard operating procedure, and the basic user training manual was developed and the capacity of government staff was built on the operations of the registry. CONCLUSION: In establishing a functional and sustainable registry, learning from experiences is essential in shaping acceptable, sustainable, and scalable approaches. Instituting governance structures that include and involve policymakers, health managers and users is of great importance in the design, planning, implementation, and decentralization stages. In addition, developing standardized tools based on the health system's needs and instituting supportable mechanisms for data flow and use for policy, planning, development, and management is essential.
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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 | 0.042 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".