Consultation on the Libyan health systems: towards patient-centred services
Bibliographic record
Abstract
The extra demand imposed upon the Libyan health services during and after the Libyan revolution in 2011 led the ailing health systems to collapse. To start the planning process to re-engineer the health sector, the Libyan Ministry of Health in collaboration with the World Health Organisation (WHO) and other international experts in the field sponsored the National Health Systems Conference in Tripoli, Libya, between the 26th and the 30th of August 2012. The aim of this conference was to study how health systems function at the international arena and to facilitate a consultative process between 500 Libyan health experts in order to identify the problems within the Libyan health system and propose potential solutions. The scientific programme adopted the WHO health care system framework and used its six system building blocks: i) Health Governance; ii) Health Care Finance; iii) Health Service Delivery; iv) Human Resources for Health; v) Pharmaceuticals and Health Technology; and vi) Health Information System. The experts used a structured approach starting with clarifying the concepts, evaluating the current status of that health system block in Libya, thereby identifying the strengths, weaknesses, and major deficiencies. This article summarises the 500 health expert recommendations that seized the opportunity to map a modern health systems to take the Libyan health sector into the 21st century.
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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".