Networking in eHealth research: results of the IDRC SEARCH program evaluation
Bibliographic record
Abstract
Background: The IDRC 'Strengthening Equity through Applied Research Capacity building in eHealth' (SEARCH) funded seven research projects in Bangladesh, Burkina Faso, Ethiopia, Kenya, Lebanon, Peru and Vietnam that sought to answer questions or test solutions related to the use of Internet or mobile phone technology in strengthening health systems. The evaluation accompanied these projects over two years to answer, among others, the question how cross-grant learning interactions influenced project outcomes. Methods: The evaluation team conducted repeated interviews and on-line questionnaire surveys with the research teams and analysed the information exchanges among researchers on a SharePoint site established by IDRC. Results: The expectations of the SEARCH program in terms of cross-project learning were only partially realized. The diversity of themes, language barriers and differences in context were cited as main reasons. Non-facilitated active cross-grant networking was only observed between two teams working in English on thematically similar issues. However, networking among all projects was active during two program workshops organized by IDRC. Conclusions: Networking among research teams can increase the quality and the applicability of health systems research and potentially promote knowledge translation. Spontaneous networking across language barriers is, however, difficult. Effective global research networks require dedicated human and financial resources to keep them vibrant and alive. Keywords: e-health, refugees.
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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 | 0.181 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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, 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".