Bibliographic record
Abstract
Many strategies can be used to support the use of research evidence in decision-making. However, such strategies have been understudied in crisis zones, where decision-making may be particularly complex, many factors may influence decision-makers’ use of research evidence, and professional judgements may be particularly relied upon. Using synthesis and qualitative research methods, this dissertation examines the role of research evidence in crisis zones and strategies to support its use in decision-making. First, chapter 2 describes a critical interpretive synthesis, which drew upon a broad body of literature around evidence use in crisis zones to develop a new conceptual framework that outlines strategies that leverage the facilitators and address the barriers to evidence use in crisis zones in four systems, namely the political, health, international humanitarian aid, and health research systems. Second, in chapter 3, the focus narrows, and an embedded qualitative case study design was used to gain a deeper understanding into one of the four identified systems, the political system, and specifically the factors that influenced the use of research evidence in the governmental health policy-development processes for Syrian refugees in Lebanon and Ontario. Finally, in chapter 4, a user testing study design was used to zero-in on decision-makers’ experiences with a particular strategy within the health research system, namely an evidence website focused specifically on topics relevant in crisis zones. This dissertation provides a rich understanding of research evidence use by examining knowledge translation strategies in a setting that has been largely unexplored in the broader KT map: crisis zones. The findings from this thesis point to the need for comprehensive strategies to support evidence use in decision-making that draw upon the existing literature and are adapted for crisis zones, which can occur sequentially or simultaneously within or across the four identified systems.
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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 | 0.230 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.020 | 0.051 |
| Scholarly communication | 0.051 | 0.049 |
| Open science | 0.008 | 0.054 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".