Insights from system leaders about operationalising a knowledge translation department in the Oman Ministry of Health
Bibliographic record
Abstract
Background: Oman has prioritised enhanced efforts for supporting evidence-informed policymaking (EIPM), including establishing a knowledge translation department in the Omani Ministry of Health (MOH). Aim and objective: Our aim was to gather insights to guide the process of activating this department. Methods: We conducted a document review and in-depth, semi-structured interviews with policymakers, researchers, and stakeholders who are familiar with the Omani system. Findings: We conducted 17 interviews, which highlighted that policymakers in Oman use multiple sources of data and evidence to inform policymaking about health systems. However, several challenges to using evidence were identified, including low quality and limited availability of local evidence, system fragmentation, low interest in research, and lack of skills, capacity and time for finding, synthesising and using research evidence. Five possible activities for the department were refined with participants: building capacity, finding evidence, sparking action, embedding supports, and evaluating innovations. Participants viewed each of these activities as equally important and they should be pursued simultaneously. However, when asked to rank the most important option, participants identified capacity building as the most important to enable cultural changes needed within the MOH. Discussion and conclusions: This study provides insights for activating the knowledge translation department in the Omani MOH. Fully operationalising the department will require convening a codesign process to reach consensus on the scope of the activities undertaken by the department. Implementation will also require capitalising on the relevant experience of highly qualified staff and existing infrastructure as well as continuing to foster a supportive climate for EIPM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".