Patient-oriented research: An essential driver of learning health system capacity development
Bibliographic record
Abstract
Canada’s health system faces a lag in implementing high-quality evidence and research-driven innovation into service delivery, while demonstrating accountability and benefit to the public. To address these challenges, Patient-Oriented Research (POR) builds teams that engage researchers, healthcare providers, decision-makers, and most importantly, patients (people with lived and living experience) in the process of generating and applying evidence to inform health services and decision-making. A Learning Health System (LHS) systematically integrates external evidence with internal data and experience and puts that knowledge into practice in a continuous cycle. Using a POR/LHS example from a BC health authority, we describe nine enablers required to support LHS capacity development. The LHS case study, Walk With Me, addresses a health system high-priority topic: the toxic drug crisis. Understanding the value of learning health systems, along with the enablers required to support and implement them, will empower health leaders to champion and orchestrate positive change.
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 | 0.235 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.027 | 0.014 |
| Open science | 0.005 | 0.041 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 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".