Canadian Frailty Network (CFN) National Conference Abstracts
Bibliographic record
Abstract
One of the components of the Canadian Frailty Network (CFN) Interdisciplinary Training Program is for HQPs to disseminate their work through publication and meetings. The main dissemination event of CFN is the annual conference; the 3rd CFN Annual Conference on Improving Care for the Frail Elderly was held in Toronto, September 27-29, 2015 and the 4th CFN Annual Conference was held in Toronto, on April 23-24, 2017. The goal for both conferences was to bring together key researchers, practitioners, educators, policy-makers, advocates, and organizations devoted to improving health care for the seriously ill, frail elderly, and to highlight HQP research. All 2015 and 2016 HQPs in the Summer Student Award Program, the Interdisciplinary Fellowship Project, and project HQP involved with a CFN-funded project submitted an abstract for their affiliated conference. The abstracts were reviewed for quality, and the authors presented them as posters during the conferences. In what follows, we present the compilation of research abstracts that were presented by CFN HQP at the 3rd and 4th annual conferences. The annual conference will continue to be expanded in coming years, and next year we will accept abstracts from all researchers who are engaged with the seriously ill, frail elderly.
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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.211 | 0.039 |
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".