Bibliographic record
Abstract
Background: There is limited evidence describing how to build capacity for health policy and systems research (HSPR) in low- and middle- income countries (LMICs). The WHO HPSR Alliance (The Alliance) developed the Research to Enhance the Adaptation and Implementation of Health Systems Guidelines (RAISE) initiative. The RAISE initiative aims to build capacity for HSPR in LMICs and supports six LMIC research teams using HSPR to adapt and/or implement health systems guidelines within a two-year funding period. The Alliance commissioned the Knowledge Translation (KT) Program at St. Michael's Hospital-Unity Health Toronto to act as a Technical Support Centre (TSC) to provide ongoing scientific support and capacity building for the RAISE teams. Objectives: The objectives of this study are to (1) develop, monitor and evaluate the TSC capacity building program; (2) determine the impact of the TSC activities on research capacity and practice, and impact of the RAISE initiative on stakeholder engagement, guideline adaptation, and implementation; and (3) advance the science on common barriers and facilitators faced by LMICs who aim to adapt and implement health systems guidelines. Methods: The TSC provides interventions to support capacity building, including: in-person workshops, an online webinar series, online discussion boards, in-country visits and ongoing communication and coaching calls with the RAISE teams. Using a mixed methods approach, we will administer surveys, conduct key informant interviews and perform a document review to achieve the study objectives. Outcomes of Interest: We will describe our process of iteratively adapting the support provided based on the needs identified by the RAISE teams. Additionally, we will evaluate the implementation quality of the TSC capacity building program, researchers’ and knowledge users’ self-efficacy in KT research utilization, perceived impact of the RAISE portfolio on stakeholder engagement, guideline adaptation, and implementation, and barriers and facilitators to health systems guideline adaptation and/or implementation in LMICs. Anticipated impact: This research will advance the science on a) capacity building for HSPR and KT and b) common barriers and facilitators to health system guideline implementation in LMICs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.806 | 0.889 |
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; both teacher heads agree on what is shown here.
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".