Filling the gap: Mental health and psychosocial paramedicine programming in Ontario, Canada
Bibliographic record
Abstract
Paramedics respond to acute medical and trauma emergencies in the community and transport patients to emergency departments (ED). In some cases, paramedics are not only attending calls for mental health and psychosocial care but are also connecting individuals with more appropriate services to address their needs. This study qualitatively explores to what extent there are promising practices to be learned from paramedic services that are connecting patients to mental health and psychosocial programming. The study is organised as follows. In terms of the methods, we conducted a critical ethnographic case study of mental health and psychosocial care within paramedic services in Ontario, Canada. Interviews were conducted with frontline paramedics (n = 31), paramedic services management (n = 5), educators at paramedic college programmes (n = 5) and Base Hospital physicians/directors (n = 5). Work observations were also performed in three paramedic services, with multiple crews across different shifts (n ~90 hr). The study findings outline three promising practices: diversion programmes that transfer patients to a destination other than the ED; crisis response teams that attend calls identified as involving mental health and community paramedicine programmes including referral programmes. We outline the social, political and economic conditions in which these programmes were established and are provided. We also describe the conditions required to enable connecting patients to non-ED supports. The benefits of implementing specific programming for mental health-related calls within paramedic services are discussed, as well as the importance of reaching beyond the prehospital and mental healthcare system to comprehensively and preventatively address mental health needs. Tensions are explored related to running programmatic interventions for mental health by paramedic services. We conclude by noting some public policy-level challenges including the need to focus more broadly on prevention and address the social determinants of health to aid the de-escalation of distress.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".