A health knowledge brokering intervention in a district of Burkina Faso: A qualitative retrospective implementation analysis
Bibliographic record
Abstract
BACKGROUND: A knowledge brokering (KB) intervention was implemented in Burkina Faso. By creating partnerships with health system actors in one district, the broker was expected to assess their knowledge needs, survey the literature to provide the most recent research evidence, produce various knowledge translation tools, and support them in using research to improve their actions. The purpose of this study was to analyze the key factors that influenced the KB project and to make recommendations for future initiatives. METHODS: The qualitative design involved a single case study in which the KB intervention implementation was evaluated retrospectively. Data came from interviews with the intervention team (n = 4) and with various actors involved in the intervention (n = 16). Data from formative evaluations conducted during the KB implementation and observation data from a two-month field mission were also used. Two conceptual frameworks were combined to guide the analysis: the Consolidated Framework for Implementation Research (Damschroder et al., 2009) and the Ecological Framework (Durlak & DuPre, 2008). RESULTS: Various KB activities were conducted during the first two years of implementation at the local level. The project came to an early end following vain efforts to relocate the intervention at the central level in order to further influence the policy process. Certain shortcomings in the implementation team negatively influenced the implementation: inadequate leadership, no shared vision regarding the reorientation of the intervention, challenges related to the KB role, and lack of frank communications internally. Other impediments to the intervention's deployment included local actors' lack of decision-making authority, the unavailability of resources and of organizational incentives for involvement in the KB intervention, and contextual challenges in accessing the central level. However, the KB strategy presented several strengths: collaborative development, support provided to local partners by the broker, and training opportunities and support provided to the broker. CONCLUSIONS: More attention must be paid to intervention planning, partners' engagement, human, financial and technical resources availability, continuous development of skills and of communications within the KB team, and periodic assessment of potential obstacles related to the complexity of the system within which the intervention has been implemented. Using implementation science frameworks when developing KB strategies in the West African context should be promoted.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".