Kenyan Nurses Involvement in National Policy Development Processes
Bibliographic record
Abstract
The aim of this study was to critically examine how nurses have been involved in national policy processes in the Kenyan health sector. The paper reports qualitative results from a larger mixed method study. National nonnursing decision-makers and nurse leaders, and provincial managers as well as frontline nurse managers from two Kenyan districts were purposefully selected for interviews. Interviews dealt with nurses' involvement in national policy processes, factors hindering nurses' engagement in policy processes, and ways to enhance nurses' involvement in policy processes. Critical theory and feminist perspectives guided the study process. Content analysis of data was conducted. Findings revealed that nurses' involvement in policy processes in Kenya was limited. Only a few nurse leaders were involved in national policy committees as a result of their positions in the sector. Critical analysis of the findings revealed that hierarchies and structural factors as well as nursing professional issues were the primary barriers constraining nurses' involvement in policy processes. Thus, there is need to address these factors both by nurses themselves and by nonnursing decision makers, in order to enhance nurses engagement in policy making and further the contribution to quality of services to the communities.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 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".