The making of nursing practice Law in Lebanon: a policy analysis case study
Bibliographic record
Abstract
BACKGROUND: Evidence-informed decisions can strengthen health systems, improve health, and reduce health inequities. Despite the Beijing, Montreux, and Bamako calls for action, literature shows that research evidence is underemployed in policymaking, especially in the East Mediterranean region (EMR). Selecting the draft nursing practice law as a case study, this policy analysis exercise aims at generating in-depth insights on the public policymaking process, identifying the factors that influence policymaking and assessing to what extent evidence is used in this process. METHODS: This study utilized a qualitative research design using a case study approach and was conducted in two phases: data collection and analysis, and validation. In the first phase, data was collected through key informant interviews that covered 17 stakeholders. In the second phase, a panel discussion was organized to validate the findings, identify any gaps, and gain insights and feedback of the panelists. Thematic analysis was conducted and guided by the Walt & Gilson's "Policy Triangle Framework" as themes were categorized into content, actors, process, and context. RESULTS: Findings shed light on the complex nature of health policymaking and the unstructured approach of decision making. This study uncovered the barriers that hindered the progress of the draft nursing law and the main barriers against the use of evidence in policymaking. Findings also uncovered the risk involved in the use of international recommendations without the involvement of stakeholders and without accounting for contextual factors and implementation barriers. Findings were interpreted within the context of the Lebanese political environment and the power play between stakeholders, taking into account equity considerations. CONCLUSIONS: This policy analysis exercise presents findings that are helpful for policymakers and all other stakeholders and can feed into revising the draft nursing law to reach an effective alternative that is feasible in Lebanon. Our findings are relevant in local and regional context as policymakers and other stakeholders can benefit from this experience when drafting laws and at the global context, as international organizations can consider this case study when developing global guidance and recommendations.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".