ABI and Addiction/Mental Health Collaborative
Bibliographic record
Abstract
BackgroundSoutheastern Ontario (SEO) is a large, predominantly rural area with an incidence of Traumatic Brain Injury (TBI) (excluding concussion) over the most recent 6 fiscal years of 920: 11.3% had a mental health diagnosis at the time of the TBI. An implementation survey relating to a Guideline for rehabilitation of adults with moderate-to-severe TBI released in 2016, indicated that two high priority best practice recommendations relating to collaboration and continuity of care in TBI and mental health and/or addiction issues, were not fully implemented in this region.InterventionA 20-member, multi-sector (rehabilitation, mental health, addictions, corrections and womenu2019s shelter) and lived experience working group met over several months to develop a mechanism for addressing the complex, unmet needs of adults in this region with moderate-to-severe ABI complicated by mental health/addiction issues. The working group developed the referral process, inclusion and exclusion criteria, consent process and forms, discussion format, and performance indicators. It was decided to develop a Collaborative in each of 3 sub-areas and this Collaborative has been piloted in one of the sub-areas with data collection relating to process, outcome and sustainability. OutcomesThe Collaborativeu2019s purpose is to develop capacity to address complex, unmet needs using a shared-care model incorporating discussion about sequential/concurrent care. The SEO ABI System Navigator coordinates monthly u201crounds,u201d with 7 Collaborative members, including Physiatry, Psychiatry, Addictions Medicine, and community service providers, to address unmet needs of people meeting specified criteria including presence of a high-risk situation defined as u201cindividuals or families facing a number of risk factors that affect multiple areas and in all likelihood will lead to something bad happening, and happening soon.u201d The discussion was to review only those clients who give consent and to monitor the number of clients who do not consent. Baseline data was gathered over an 8-week period. Providers identified 30 people meeting the inclusion criteria: 80% male; 53% urban, 17% rural, 17% incarcerated; 67% required supported accommodation, 37% required psychosocial intervention, 30% required addiction service; barriers to care through usual means were client refusal (50%), no psychiatrist (23%), and long waiting lists (23%).The SEO ABI System Navigator is tracking performance indicators related to volume of referrals and unmet needs, referral timeframes, percentage of needs met, barriers to meeting needs, and use of healthcare system and patient flow in SEO. Benefits of the Collaborative include:u2022tEnhanced provider understanding of services, mandates, roles, and admission criteria u2022tEnhanced collaboration amongst service providersu2022tEnhanced understanding of how best to identify and work with people with ABIu2022tBetter communication amongst service providers about how best to meet the needs of people with ABIu2022tImproved continuity and efficiency of care
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.060 | 0.197 |
| Open science | 0.023 | 0.041 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".