Consultative Workshop Proceedings of the Canadian Team to Improve Community-Based Cancer Care along the Continuum
Bibliographic record
Abstract
The multidisciplinary pan-Canadian canimpact (Canadian Team to Improve Community-Based Cancer Care Along the Continuum) group is studying how to improve cancer care for patients in the primary care setting. A consultative workshop hosted by the team took place on 31 March and 1 April 2016 in Toronto, Ontario. The workshop included 74 participants from 9 provinces, with representation from primary care, cancer specialties, international liaisons, knowledge users, researchers, and patients. On the agenda were presentations from canimpact phase 1 projects including (1) qualitative studies on the perspectives of survivors and health care providers about continuity and coordination of care; (2) an environmental scan and systematic review of existing initiatives designed to improve care integration; (3) population-based administrative health database analyses related to breast cancer diagnosis, treatment, and survivorship; and (4) a qualitative study on the experiences, desired roles, and needs of primary health care providers with respect to personalized medicine. In addition, there were presentations on two possible intervention approaches, including nurse navigation and the eConsult system. Based on the information presented, participants worked in small groups to develop recommendations for phase 2, which will involve development and evaluation of an intervention to improve the integration of care between primary care providers and cancer specialists. After a process of deliberation and voting, workshop participants recommended testing the implementation of eConsult in the oncology setting to determine whether it improves relationships, communication, knowledge sharing, and connections between family doctors and cancer specialists; and, to improve system navigation, evaluating eConsult in existing nurse navigator programs, if feasible.
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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".