TOWARDS A MORE ETHICAL GLOBAL HEALTH RESEARCH: A CASE STUDY OF KNOWLEDGE TRANSLATION WITHIN THE ALERT COMMUNITY TO PREPARED HOSPITAL CARE CONTINUUM IMPLEMENTATION RESEARCH PROJECT
Bibliographic record
Abstract
The complex form of knowledge translation (KT) that takes place when community information is used to inform interventions within implementation research (IR) has not been explored within the KT academic literature. Furthermore, research fatigue has not been taken into consideration when evaluating KT processes in the academic literature. Research fatigue occurs when a community has had too much research done to it without seeing proportionate benefit, and become weary of the process. This is an important factor to consider because successful knowledge use within IR projects has the opportunity to reduce risk for research fatigue through community perception of change based on participation in research, whilst knowledge collection without a perceived change has been shown to increase the risk. Considering this premise, the objective of this thesis was to investigate the KT process within a maternal and child health IR project entitled the Alert Community to Prepared Hospital Care Continuum Project. The IR project was funded as development aid through a branch of Global Affairs Canada. To study this KT process, a case study was designed that included a document review, participant observations, interviews with the members of the research team, and a focus group discussion. Studying the research team’s KT process, there wasn’t a structured KT or research framework, which hindered community knowledge incorporation. Additionally, weaknesses in data analysis due to time constraints and a lack of statistical expertise resulted in survey data not impacting continued implementation. However, the community-based design of the IR project allowed tacit knowledge to be integrated via KT based upon knowledge attained through relationship building and community consultations. Lastly, the structure of development aid itself was found to be problematic, as it reinforced global power inequities through funding restrictions, funding timelines, and through the physically separation of donor wealth from local knowledge. This can be addressed moving forward by doing anti-oppressive work both inside and outside of academia.
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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 | 0.085 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.028 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".