Capturing lessons learned from evidence-to-policy initiatives through structured reflection
Bibliographic record
Abstract
BACKGROUND: Knowledge translation platforms (KTPs), which are partnerships between policymakers, stakeholders, and researchers, are being established in low- and middle-income countries (LMICs) to enhance evidence-informed health policymaking (EIHP). This study aims to gain a better understanding of the i) activities conducted by KTPs, ii) the way in which KTP leaders, policymakers, and stakeholders perceive these activities and their outputs, iii) facilitators that support KTP work and challenges, and the lessons learned for overcoming such challenges, and iv) factors that can help to ensure the sustainability of KTPs. METHODS: This paper triangulated qualitative data from: i) 17 semi-structured interviews with 47 key informants including KTP leaders, policymakers, and stakeholders from 10 KTPs; ii) document reviews, and iii) observation of deliberations at the International Forum on EIHP in LMICs held in Addis Ababa in August 2012. Purposive sampling was used and data were analyzed using thematic analysis. RESULTS: Deliberative dialogues informed by evidence briefs were identified as the most commendable tools by interviewees for enhancing EIHP. KTPs reported that they have contributed to increased awareness of the importance of EIHP and strengthened relationships among policymakers, stakeholders, and researchers. Support from policymakers and international funders facilitated KTP activities, while the lack of skilled human resources to conduct EIHP activities impeded KTPs. Ensuring the sustainability of EIHP initiatives after the end of funding was a major challenge for KTPs. KTPs reported that institutionalization within the government has helped to retain human resources and secure funding, whereas KTPs hosted by universities highlighted the advantage of autonomy from political interests. CONCLUSIONS: The establishment of KTPs is a promising development in supporting EIHP. Real-time lesson drawing from the experiences of KTPs can support improvements in the functioning of KTPs in the short term, while making the case for sustaining their work in the long term. Lessons learned can help to promote similar EIHP initiatives in other countries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.200 | 0.248 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".