INSIGHTS GAINED FROM THE DEVELOPMENT OF COMMUNITY PARAMEDICINE PROGRAMS IN CANADA
Bibliographic record
Abstract
Older adults are the highest users of paramedical and ambulance services. These older adults typically struggle with polymorbidity, functional impairments, and social frailty. Most calls received by paramedics from older adults are neither time-sensitive nor immediately life-threatening. Paramedics are increasingly seeing older individuals in pre-crisis situations who need more support to help them age in place. The need to work differently to meet the needs of older adults has given strength to the community paramedicine movement across Ontario. In 2014, Ontario’s Ministry of Health invested $6 million to support the development of 30 community paramedicine demonstration projects that allow paramedics to fill unique care gaps, and better integrate care for older patients. Our proposed symposium will highlight insights gained from the community paramedicine demonstration projects through four talks: 1) “First-Year Outcomes of the MOHLTC Funded Community Paramedicine Demonstration Projects” by Dr. Samir Sinha, 2) “Establishing the Effectiveness of the Independence at Home Community Paramedicine Model”, by Ms. Nicoda Foster, 3) “Lessons Learned from Renfrew County’s Community Paramedic Response Unit Program” by Mr. Michael Nolan and 4) “Evaluating the Effectiveness of Community Referrals by Emergency Medical Services” by Dr. Amol Verma. The goal of our symposium is to enhance the awareness of attendees of community paramedicine, the diverse models of care it can represent and their ability to positively impact patients and providers and the health systems. The symposium will conclude with an interactive discussion exploring the facilitators and barriers to the implementation of effective community care models for the elderly.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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 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".