Building a Culture of Evidence-Informed Decision Making in the Community
Bibliographic record
Abstract
Growing fiscal pressures on health departments both provincially and locally necessitate tough decisions to be made. Although evidence-informed decision making may be commonly used for clinical decision making, the notion of evidence-informed decision making for managing physician office practice processes, primary care, long-term care, or continuing care is limited. In healthcare, much data are collected, yet only a small percentage is actually used in meaningful ways. The Executive Training for Research Application (EXTRA) program strives to not only assist healthcare executives in acquiring necessary skills but also aims to lead cultural change in the Canadian healthcare system. This article describes three brief examples in which a vice president and director with EXTRA training have started to explore and use data to drive change in the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.182 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.028 | 0.080 |
| Scholarly communication | 0.032 | 0.018 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.008 | 0.031 |
| Insufficient payload (model declined to judge) | 0.002 | 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".