Reflections on Conversations with Robert Bell and Michael Guerriere: What Is Relevant Research?
Bibliographic record
Abstract
Two decision-makers from the acute-care sector weigh in on the issue of relevant research.Between the two of them they look for patient-defined research, evidence to support the conclusions, information that can lead to interventions designed to improve quality and outcomes and defined control mechanisms to properly identify the practices that improved the system.Three examples are cited and discussed.The context is set by comments from one of Canada' s leading researchers and the use of research from one of this decade' s most lauded system turnarounds. RésuméDeux décideurs du secteur des soins actifs se prononcent sur la question de la recherche pertinente.À eux deux, ils cherchent des travaux de recherche axés sur le patient, des preuves pour étayer les conclusions, des renseignements pouvant mener à des interventions conçues pour améliorer la qualité et les effets, ainsi que des mécanismes de contrôle définis permettant de cerner les pratiques qui contribuent à améliorer Reflections on Conversations with Robert Bell and Michael Guerriere:What Is Relevant Research?Réflexions sur des conversations avec Robert Bell et Michael Guerriere : La recherche pertinente : qu' est-ce que c' est au juste?
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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 | 0.128 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.041 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.060 | 0.138 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".