A Novel Emergency Telepsychiatry Program in a Canadian Urban Setting: Identifying and Addressing Perceived Barriers for Successful Implementation: Un nouveau programme de télépsychiatrie d’urgence en milieu urbain canadien: Identifier et aborder les obstacles perçus d’une mise en œuvre réussie
Bibliographic record
Abstract
OBJECTIVES: To report on the perceived barriers surrounding the use of telepsychiatry for emergency assessments and our approach to overcoming those barriers to achieve successful implementation of a program to increase access to emergency psychiatric assessment in a Canadian urban setting. METHODS: We conducted a survey of emergency care staff to inform the implementation of an emergency telepsychiatry program in the urban setting of Winnipeg, Manitoba, where hospitals have variable on-site emergency psychiatric coverage. We analyzed survey responses for perceived barriers we would need to address in implementation. We employed implementation strategies for each barrier and scaled the program to three sites over the first year. Data from the first year were collected including number of telepsychiatry assessments, reasons for referral, wait time, and percentage of patient transfers avoided. RESULTS: = 111) had little prior exposure to telepsychiatry, but the majority were open to its use for emergency psychiatric assessments in the region. We identified three categories of perceived barriers: clinical, logistical/technical, and readiness barriers. Implementation planning addressed each barrier, and a hub-and-spoke program was launched. After the first year, the program had one hub serving three spokes, and 243 emergency telepsychiatry assessments had been completed. After 12 months, we were avoiding 65% of patient transfers. CONCLUSIONS: By conducting a user survey to identify perceived barriers, and addressing these during implementation, we successfully scaled our emergency telepsychiatry program across our region. Our report of this experience may benefit others attempting to implement a similar program.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".