Assessing and managing children with urgent psychiatric needs during COVID-19
Bibliographic record
Abstract
To reduce person-to-person transmission of COVID-19 Canada implemented restrictions at com-munity and hospital levels At Kingston Health-Science-Centre most outpatient psychiatric services, including the Child and Adolescent Mental Health Urgent Consult Clinic (CAMHUCC), were transitioned from in-person to virtual clinics The aim of this study is to examine changes in referrals to CAMHUCC and in management of youth referred for urgent psychiatric consult Methods: This retrospective study compares all patients <18years assessed by the CAMHUCC after the switch to the virtual clinic model (March to May 2020;COVID group), with patients who were assessed for the same time period in 2019 (Pre-COVID group) Groups are compared by their demographic and clinical characteristics Results: All patients agreed to the assessment through telepsychiatry There are less referrals during the COVID than in the Pre-COVID period (63 vs 84) Demographic and clinical characteristics between the two groups are without significant difference In the COVID group there is a slightly higher number of indigenous children and patients diagnosed with adjustment disorder There is no significant dif-ference in recommendations between the groups However, implementation of recommendations differs in that those in the COVID group requiring behavior intervention and or psychoeducational assessment, could not be provided the service as these were not feasible via OTN Conclusion: The pandemic-related restrictions and the switch to an online clinic model does not negatively impact urgent psychiatric assessment and management of youth but does affect available resources Further research is warranted to evaluate the long-term effect of those changes
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".