Exploring the Views of Emergency Department Staff on the Use of Videoconferencing for Mental Health Emergencies in Southwestern Ontario
Bibliographic record
Abstract
Patients presenting to a rural emergency department (ED) with mental health symptoms have difficulty accessing services of mental health professionals [1,2]. Videoconferencing (VC) has been found to improve patient access to health services that require specialist care in rural EDs [3,4,5]. Although previous studies highlight the benefit of using VC for patients presenting with mental health emergencies, no study has investigated the current views and use of VC for mental health emergencies in EDs in Southwestern Ontario [3,5,6]. To explore the views of ED staff regarding the use of VC in mental health emergencies, structured telephone interviews were conducted with representatives from EDs in the Erie St. Clair and Southwest Local Health Integration Networks (LHIN). Participants noted that using VC for mental health emergencies may improve patient experience and benefit crisis response teams. VC was perceived by some participants as a means to expedite the direct assessment of a patient presenting with a mental health emergency by a mental health specialist. However several participants stated that using VC for mental health emergencies strains ED resources. Lack of use and difficulty accessing a psychiatrist were identified as potential barriers to implementing the use of VC for mental health emergencies.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".