More stakeholders with fewer issues instead of fewer issues with more stakeholders
Bibliographic record
Abstract
IntroductionThere is no provincial platform in Ontario to connect persons who are living with persistent symptoms of concussion. Approximately 157,000 Ontarians are diagnosed with a concussion each year and roughly 15 to 20% (23,550 u2013 31,440) go on to live with persistent symptoms which negatively affects their quality of life and places a significant burden on the healthcare system. By connecting existing stakeholders (caregivers, persons with lived experience and their family members) to a provincial platform, all regions within Ontario can be represented allowing common issues to be identified and prioritized for people experiencing persistent symptoms. This will help patients receive the right care, at the right time, from the right providers. This approach is based on the Ontario Spinal Cord Injury (SCI) Alliance which represents approximately the same number of stakeholders in Ontario (32,000) and has been effective in sustaining government relations and funding. MethodsThe Provincial Advisory Network has been created to not only unify the voice of persons with lived experience but to allow for a more coordinated and systematic approach when organising collective political strategy toward government relations. The Government of Ontario has divided the province (over 14 million people) into 14 health regions and the Provincial Advisory Network has established coordinators within each region to ensure equal representation of stakeholders. The Provincial Advisory Network is capitalizing on the brain injury association networks to help formulate strategies and engage people from all ages and all types of concussion incidences (falls, sports, motor vehicle collisions etc.). The Ontario SCI Alliance is a key consultant in this methodology. Results The feedback which is retrieved in the form of surveys, questionnaires, focus groups, telecommunication and committee meetings from each of the 14 regions is continually analyzed to determine if it has local or provincial implications and whether it needs to be consulted with the healthcare sector, social services sector and/or different levels of government. The network is also used to disseminate materials on emerging trends in policies, benefits and issues relating to concussion in addition to helping regions learn from one another on how to better approach system challenges. The Provincial Advisory Network currently has over 300 stakeholders and is projected to grow to 4000-5000 stakeholders in three years time. Discussion The Provincial Advisory Network supports the development of an articulated provincial strategy to amplify the voice of the community of people living with persistent symptoms of concussion. This provincial platform is helping identify the most important issues to address and acts a two-way communication channel between the healthcare system and community. This platform will serve as a foundation to address the full spectrum of traumatic brain injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".