The <i>Journal of Comorbidity</i> Affiliates with the North American Primary Care Research Group
Bibliographic record
Abstract
The Journal of Comorbidity is pleased to announce that it has extended its society collaborations by forming another important partnership with the North American Primary Care Research Group (NAPCRG), a renowned multidisciplinary organization for primary care researchers based in North America and beyond. The new partnership reflects an ongoing commitment of both the Journal of Comorbidity and NAPCRG to strengthen and broaden their ties with the primary care community and to foster the dissemination of innovative research in the field of comorbidity and multimorbidity. The partnership marks a celebration of the contribution of NAPCRG and its global members to conducting and reporting high-quality research on comorbidity/multimorbidity over the past 10 years. NAPCRG has been instrumental in growing awareness and developing the field of comorbidity/multimorbidity through various workshops, seminars, and plenaries at its annual meetings held in North America. Furthermore, in 2007, NAPCRG helped organize a meeting funded by the Canadian Institutes of Health on the theme of multimorbidity; attended by over 30 participants from eight countries, this led to the creation of the International Research Community on Multimorbidity (IRCMo) [1]. IRCMo now has almost 1,000 subscribers who are connected through a blog that showcases the most recent developments and publications on comorbidity/multimorbidity research. IRCMo also publishes regular updates on publications that are particularly important in terms of capacity building in research. Journal of Comorbidity 2017;7(1):114–116
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.043 | 0.028 |
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".