Salient stakeholders: Using the salience stakeholder model to assess stakeholders’ influence in healthcare priority setting
Bibliographic record
Abstract
Stakeholders play an important role in health priority setting, and their roles have been discussed in the literature, mainly in relationship to their power. An emerging body of literature is focusing on the legitimacy of the stakeholders. Using the case of the Uganda health system, the overall aim of this paper is to assess the utility of the salience stakeholder analysis framework in identifying the most salient stakeholders in health-care priority setting. Methods: This was a qualitative case study involving 57 key informant interviews with national and district level policy makers and a review of policy documents. Interview data were analyzed using QSR NVivo10 qualitative data analysis software. Analysis was guided by the salience stakeholder analysis framework. Findings: Among the eight groups of stakeholders identified by the respondents, the politicians were found to be the most salient stakeholders. However, stakeholders' salience varied depending on the type of decision, the nature of health issue and how and who tabled the health issue. Conclusion: The salience stakeholder analysis framework, originating from the business management and political science disciplines, provided a more comprehensive stakeholder analysis by supporting the concurrent consideration of power, legitimacy and urgency in stakeholder analysis for health care priority setting.
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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 | 0.031 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".