Experiences of nurses and midwives in policy development in low- and middle-income countries: Qualitative systematic review
Bibliographic record
Abstract
Background: Nurses provide 90% of health care worldwide, yet little is known of the experiences of nurses and midwives in policy development in low- and middle-income countries (LMICs). Objective: To identify, appraise and synthesize the qualitative evidence on the experiences of nurses' and midwives' involvement in policy development LMICs. Design: A qualitative systematic review using modified Joanna Briggs Institute (JBI) methodology. Setting: Low and middle-income countries Participants: Nurses' and midwives' involved in policy development, implementation, and/or evaluation. Methods: A systematic search was undertaken across nine databases to retrieve published studies in English between inception and April of 2021. Screening, critical appraisal, and data extraction was undertaken by two independent reviewers. Results: Ten articles met inclusion criteria. All studies were published between 2000 to 2021 from a variety of LMICs. The studies were medium to high quality (70-100% critical appraisal scores). Four major themes were identified related to policy development: 1) Marginal representation of nurses; 2) Determinants of nurses' involvement (including at the individual, organization, and systematic level); 3) Leadership as a pathway to involvement; 4) Promoting nurses' involvement. Conclusion: All studies demonstrated that nurses and nurse midwives continue to be minimally involved in policy development. Findings reveal reasons for nurses' limited involvement and strategies to foster sustained engagement of nurses in policy development in LMICs. To enhance their involvement in policy development in LMICs, change is needed at multiple levels. Systemic power relations need to be reconstructed to facilitate more collaborative interdisciplinary practices with nurses co-leading and co-developing health care policies.
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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 | 0.063 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".