<scp>COVID</scp>‐19 pandemic's effects and telehealth in Early Psychosis Services of Quebec, Canada: Will changes last?
Bibliographic record
Abstract
AIM: To explore the impacts of the COVID-19 pandemic first wave in Quebec, Canada on practices in early intervention services (EIS) for first-episode psychosis, including reorganization of clinical and administrative practices and teleconsultation use. METHODS: Adopting a cross-sectional descriptive study design, a 41 questions online survey was sent to the team leaders of all the 33 Quebec EIS, of which 100% responded. Data were collected from 18 May to 4 June 2020 and analysed using descriptive statistics and content analysis. Programmes were categorized as urban/non-urban and results were compared between these. RESULTS: All 33 existing Quebec EIS (16 urban and 17 non-urban) completed the survey. Among them, 85% did not experience redeployment of EIS team staff and 58% reported stable frequency of patient interactions, either in-person or through telemedicine. During the studied period, 64% of programmes reported that all professionals used teleconsultation at least occasionally. However, 73% of programmes, mostly in non-urban areas, reported some limitations regarding clinicians' degree of ease with teleconferencing platforms and half of EIS could not access technical support to use them. The majority of EIS (94%) expressed interest to participate in a training program about the use of technologies for teleconsultations. Many smaller clinics reported interest in offering multiregional/multiclinics group teletherapy, therefore merging their pool of patients and clinical staff workforce. CONCLUSIONS: Further studies are warranted to improve access to and use of technology-mediated treatment, which seems to be a promising alternative to provide high-quality mental healthcare during the COVID-19 pandemic and beyond.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".