A case study of the impact of the Newfoundland and Labrador Centre for Applied Health Research (NLCAHR) Contextualized Health Synthesis Research Program (CHRSP) on healthcare in Newfoundland and Labrador
Bibliographic record
Abstract
Applied health research, distinct from fundamental biomedical research, encompasses scientific exploration and a societal mission, emphasizing relevance to healthcare systems. Applied health services research specifically applies research methods to address real-world healthcare delivery, management, and policy issues, aiming to generate evidence-based solutions for improving quality, effectiveness, and accessibility. This type of research often involves collaboration among researchers, healthcare providers, and policymakers, utilizing various methods such as randomized controlled trials, observational studies, and qualitative research. The findings contribute to informed decisionmaking, guiding policy development and practices to enhance population health. The Contextualized Health Research Synthesis Program (CHRSP), introduced by the Newfoundland and Labrador Center for Applied Health Research (NLCAHR), is a vital initiative addressing the knowledge-to-action (KTA) gap with tailored evidence for Newfoundland and Labrador (NL). As the program surpasses its 15th year, assessing its impact, strengthening its mandate, and sharing lessons with other jurisdictions facing similar challenges is necessary. The thesis aims to document key lessons from CHRSP, identifying pathways for increased impact. By evaluating the program's processes and impact through interviews with past and current clients, it seeks to provide insights for future improvements. Some key findings highlighted the importance of contextualizing research evidence to improve healthcare outcomes, the role of CHRSP in decision-making, and the benefits of engagement in the research process. The study also identified barriers to change that must be addressed to improve healthcare outcomes in NL.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".