Practice implications and clinical observations: Virtual care for a military/Veteran population during the COVID-19 pandemic
Bibliographic record
Abstract
TRANSITION TO VIRTUAL CAREIn late March 2020, as the COVID-19 pandemic rapidly took hold of daily life in Canada, St. Joseph's Health Care London's Operational Stress Injury (OSI) Clinic, operating out of London, Hamilton, and Toronto, Ontario, rapidly moved to provide virtual services.Th e clinic is federally funded by Veterans Aff airs Canada (VAC) to provide assessment and treatment to Canadian Armed Forces (CAF) and Royal Canadian Mounted Police (RCMP) personnel and Veterans as well as their families, and is part of the larger OSI Clinic Network.In order to minimize the impact on patient care, the OSI Clinic leadership team, as well as the larger organizational leadership, quickly developed processes for transforming the face-to-face care to which clinicians and patients have long been accustomed to virtual care.Months later, clinicians and patients are growing more comfortable with virtual care in the same way we have all learned to navigate physical distancing and other facets of the "new normal."At this phase in the pandemic response, I had the opportunity to refl ect upon practice implications of transitioning to virtual care, and my personal observations of treating the military/Veteran population during this extraordinary time, which is considered best practice.1 PRACTICE IMPLICATIONS FOR VIRTUAL PSYCHOTHERAPYAs regulated health professionals, reviewing limits of confi dentiality and safeguarding privacy are standard practices within the clinic walls.With a transition to virtual care, it became clear new permutations of these ethical cornerstones were necessary.For example, the confi dentiality inherent in face-to-face communication
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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 | 0.014 | 0.082 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".