Mental health diversion from hospital emergency departments : assessing a joint effort of two mental health methods and police partnerships
Bibliographic record
Abstract
Purpose: The purpose of this research was to examine both individual and joint efforts in mental health collaboration and how individual and group initiatives can lead to decreased emergency department (ED) presentations and improved client care. Local and international literature is presented as a way to inform, advise, or hypothesize about known gaps in literature. Method: Secondary data from the 2016 calendar year was accessed through Alberta Health Services (AHS). All Mental Health Act apprehensions that occurred in the City of Edmonton during the 2016 calendar year comprise the secondary data. Data surrounding Mental Health calls for service for both Urgent Services, the Police and Crisis Team, and the Edmonton Police Service was captured and analyzed with a chi-square analysis, with a focus on the call disposition. Results: Research findings indicated the presence of a mental health clinician through either a Knoxville or Separate Response Model influenced person/people with mental illness (PMI) being admitted to hospital post mental health apprehension. PMI brought to an emergency department via a mental health act apprehension without a mental health clinician were more likely to be discharged. An informal partnership (Separate Response Model) was more successful than a formal partnership (Knoxville Model) in diverting PMI away from hospital. Regardless of substance use, there was no difference in disposition between substance and non-substance related mental health apprehensions. Findings from this research indicate mental health and police collaboration improves client care through timely access to appropriate care.\n\t\tKeywords: schizophrenia, first-responder, psychiatric nurse, mental health, mental illness, addictions, bizarre behavior, suicidal, police, collaboration, Crisis Intervention Team, Knoxville Model, Birmingham Model, crisis negotiation, Canada.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".