Comments regarding “A Canadian model for providing high-quality, timely and relevant evidence to meet health system decision-maker needs: the SPOR Evidence Alliance.”
Bibliographic record
Abstract
tates access to knowledge and evidence for health decisionmakers, clinicians, and patients (Zarin et al. 2022).In this case, Cochrane Colombia has done a fantastic job in including prominent universities and independent health care providers in large cities, which are recognized in Latin America as generators of knowledge and having high standards of care, respectively.However, there is still a lot to be done in states with poor infrastructure, small cities, and remote communities, where implementing innovative strategies that respond to local needs and better patient care is necessary.Creating collaborative networks that can break the inequities in research in these populations can solve this issue (Morales-Plaza et al. 2022).The research co-leaders' model could solve some of these needs; however, unfortunately, this process is hindered by the lack of time and dedication by researchers with enough expertise and not excluding people who are not part of the academy.
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 | 0.031 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.058 | 0.059 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".