Approaches to monitoring and evaluation of knowledge translation platforms in low- and middle-income countries: A scoping review
Bibliographic record
Abstract
Background: Knowledge Translation Platforms (KTP) attempt to bridge the "know-do gap" between researchers and policymakers. This study summarized the evidence on activities, as well as methods of monitoring and evaluation projects of KTPs in Low- and Middle-Income Countries (LMICs). Methods: The Arksey and O’Malley methodology for scoping reviews was used. The databases accessed include Medline, Global Health, CINAHL, EBSCO and Cochrane library databases. Only Studies that indicated range of activities, tools or methods used in monitoring and evaluating KTP to achieve the implementation of evidence informed policymaking in LMICs were included. The key words used includes Knowledge Translation, Monitoring and Evaluation, Platforms and Low- and Middle-Income countries. Results: Total of 3150 hits were obtained from the searched databases. 750 duplicates were identified and removed resulting to 2398 articles. Using title screening, 2123 articles were excluded resulting in 275 articles for abstract screening. Abstract screening led to exclusion 246 articles, leaving 29 articles for the full-text screening. Full-text screening resulted to exclusion of 25 articles resulting to 4 articles that meet the inclusion criteria. No relevant articles were obtained from the reference list screening and grey literature search. Conclusion: Evidence shows that Case study methodology is the predominate method of evaluating KTPs. The shortest time duration from generation to use of evidence in decision making was noticed to be 1-year. The range of activities used to monitor KTP in bridging the “know do gap” includes stakeholder’s engagement, building capacity, priority setting, meeting with stakeholders, generating policy brief, litmus testing of brief, stakeholders dialogue, evidence brief and dialogue review, disseminating of findings and implementation. The minimum and maximum number of activities performed in each KTP process is 5 and 8 activities respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".