Comparing data Reported Using the National Health Management Information System and data Declared Validated on the PBF Declaration Forms in Funding Health Districts in Nasarawa State
Bibliographic record
Abstract
The National Health Management Information System (NHMIS) Was Designed To Provide Timely And Reliable Health Service Delivery Information. The Efficiency And Effectiveness Of Health Service Delivery Is Assessed By The Availability Of Quality, Complete And Timely Data. The NHMIS Policy Review Was Initiated By A Consortium Of Relevant Stake Holders Led By The Department Of Planning, Research And Statistics (DPRS) Of The Federal Ministry Of Health (FMOH) And The National Primary Health Care Development Agency (NPHCDA). The Emphasis Of The NHMIS Is To Strengthen The Health Information System-HIS In The Country And Promote The Use Of Quality Information For Evidence-Based Decision-Making At The Community, LGA, And National Levels. In Spite Of Substantial Investments, The Health Sector In Nigeria Has Made Slow Progress In Improving Its Health Indices. Thus The Nigeria State Health Investment Project(NSHIP), Through Support From WHO, Introduced The Performance-Based Financing –PBF Currently Rolled Out In Three States- Adamawa, Nasarawa, And The Ondo States To Deliver A Result-Based Approach To Improve Quantity And Quality Of Health Services Especially In The Area Of Maternal Health. Health Centers Receive Funds Directly Based On The Number Of Essential Services They Delivered And The Improved Quality Of Care. This Encouraged Health Centers To Focus On Delivering Results, And The New Funds Enabled Them To Improve Their Services. This Study Compared Data Reported Using The NHMIS And Declared Validated On The PBF Declaration Forms In Funding Health Facilities In Nasarawa State For Quarter 1 (Jan.- Mar.)2018 And Quarter 2 (Apr. – June) 2018.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".