A case study of knowledge translation and the enhanced street youth study in Saskatoon
Bibliographic record
Abstract
In the past few decades knowledge translation has emerged as a common strategy intended to improve the effectiveness of health interventions and increase the utilization of research. Underlying the emergence of knowledge translation is a belief that new research findings will increase the efficiency of the present health care system, decrease the inequitable distribution of negative health outcomes, and improve the health of individuals. One growing population that faces increased risk of negative health outcomes in Canada, and Saskatoon, is street-involved youth. The purpose of this qualitative case study is to understand how the organizational context of a national surveillance study targeting street-involved youth, the Enhanced Street Youth Study, influences knowledge translation activities in Saskatoon, Saskatchewan. This case study traces the history of knowledge translation related to the surveillances study, and describes and analyzes the knowledge translation activities that took place in early 2011. Informed by document analysis, interviews, and participant observation, this study captures the perspectives and opinions of representatives from the key organizational and individual stakeholders: the Public Health Agency of Canada, Public Health Services of the Saskatoon Health Region, local community agencies, and street-involved youth. The experiences of the diverse stakeholders reveals the central influence of history, study processes and relationships on the success of engaging youth and local agencies in knowledge translation activities. In particular the Results illuminate how limited roles for agencies and youth within study process limit the extent of their engagement in the study and related knowledge translation activities. Importantly, the thesis study sheds light on the significance of participation and power in knowledge translation activities. Furthermore, the challenges and opportunities facing knowledge translation within a public health environment are discussed, and recommendations for future knowledge translation activities are provided. Although knowledge translation holds potential to address health inequities, intentional effort to address inequities must be made throughout the entire study process, including knowledge translation activities.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".