Proceedings of the inaugural Canadian Healthcare Navigation Conference: a forum for sharing innovations and best practices in navigation services
Bibliographic record
Abstract
BACKGROUND: Individuals experiencing chronic illnesses face many physical, emotional, and social strains as a result of their illnesses, all the while trying to navigate unfamiliar territory in the healthcare system. Navigation is a strategy that can help people facing complex care needs and barriers to care in finding and accessing needed supports in the health care system. Navigators provide a patient-centred service, guiding individuals through their care plans and overcoming barriers to care. Navigation supports for individuals experiencing complex care needs have shown significant promise and have been gaining traction across Canada. METHODS: The Canadian Healthcare Navigation Conference was the first event of its kind in Canada to bring together navigation researchers, service providers, students, decision makers, and individuals with lived experience to share lessons learned, promising practices, and research findings. This event was co-hosted by the Family Navigation Project at Sunnybrook Health Sciences Centre and NaviCare/SoinsNavi at the University of New Brunswick, and took place virtually on April 15-16, 2021. RESULTS: This event spanned two days, which both began with a keynote address, one from a researcher and medical professional in navigation, and another from an individual with lived experience involved in advocacy in Canadian healthcare. Concurrent oral presentations by a variety of presenters were held following each keynote presentation. A poster session was held at the end of the first day, and a panel presentation rounded out the second day. Concurrent and poster presentations covered a range of topics pertaining to approaches to navigation, navigator roles, evaluation and quality improvement, lived experience in navigation, and navigation in the context of the COVID-19 pandemic. The panel presentation focused on identifying how the navigation field has progressed in Canada and identifying crucial next steps in navigation. These next steps were determined to be: 1) agreement on navigation-related definitions, 2) regulation and training, 3) equity, diversity, inclusion, and accessibility, 4) integrating lived experience, and 5) regional coordination. CONCLUSION: This conference was an important first step to creating a shared national conversation about navigation services so that we can continue to develop, implement, and share best evidence and practices in the field.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".