Setting the agenda for nurse leadership in India: what is missing
Bibliographic record
Abstract
BACKGROUND: Current policy priorities to strengthen the nursing sector in India have focused on increasing the number of nurses in the health system. However, the nursing sector is afflicted by other, significant problems including the low status of nurses in the hierarchy of health care professionals, low salaries, and out-dated systems of professional governance, all affecting nurses' leadership potential and ability to perform. Stronger nurse leadership has the potential to support the achievement of health system goals, especially for strengthening of primary health care, which has been recognised and addressed in several other country contexts. This research study explores the process of policy agenda-setting for nurse leadership in India, and aims to identify the structural and systemic constraints in setting the agenda for policy reforms on the issue. METHODS: Our methods included policy document review and expert interviews. We identified policy reforms proposed by different government appointed committees on issues concerning nurses' leadership and its progress. Experts' accounts were used to understand lack of progress in several nursing reform proposals and analysed using deductive thematic analysis for 'legitimacy', 'feasibility' and 'support', in line with Hall's agenda setting model. RESULTS: The absence of quantifiable evidence on the nurse leadership crisis and treatment of nursing reforms as a 'second class' issue were found to negatively influence perceptions of the legitimacy of nurse leadership reform. Feasibility is affected by the lack of representation of nurses in key positions and the absence of a nurse-specific institution, which is seen as essential for creating visibility of the issues facing the profession, their processing and planning for policy solutions. Finally, participants noted the lack of strong support from nurses themselves for these policy reforms, which they attributed to social disempowerment, and lack of professional autonomy. CONCLUSIONS: The study emphasises that the nursing empowerment needs institutional reforms to facilitate nurse's distributed leadership across the health system and to enable their collective advocacy that questions the status quo and the structures that uphold it.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".