The nursing profession in Sri Lanka: time for policy changes
Bibliographic record
Abstract
AIM: We address issues and challenges in nursing in Sri Lanka with the aim of identifying where and how policy changes need to be made. BACKGROUND: Increased global interconnectivity calls for professional leadership, research, education, and policy reform in nursing as these are identified as enhancing health workforce performance and professionalization, thereby improving health systems. SOURCES OF EVIDENCE: We draw on first-hand knowledge of health care and nursing in Sri Lanka and a recent survey of nurses at a large urban government hospital in Sri Lanka, followed by discussion and proposed action on themes identified through analysis of published and unpublished literature about the nursing profession. DISCUSSION: Policy and action are needed to: (a) establish mandatory nurse licensure in the public and private healthcare sectors; (b) implement realistic policies to further develop nursing education; (c) develop a professionalization process to support nursing autonomy and voice; and (d) promote systematic processes for educational accreditation, curriculum revision, continuing professional development, evidence-based practice, research, leadership, and information systems. CONCLUSION: There is a policy vacuum that requires careful analysis and strategic planning by formal nurse leaders. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Implementing change will require political and professional power and strategic, innovative, and evolutionary policy initiatives as well as organizational infrastructure modifications best achieved through committed multidisciplinary collaboration, augmented research capacity, bolstered nursing leadership, and promotion of partnerships with policy makers.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".