Delivering rehabilitation services during the COVID-19 pandemic: How CAF Physiotherapy is using telehealth to ensure “physical and measurable solutions to maintain and enhance operational readiness, anywhere, anytime”
Bibliographic record
Abstract
In response to COVID-19, the Canadian Armed Forces (CAF) activated Operation LASER, a force health protection strategy to preserve its operational capabilities. Operation LASER resulted in a quarantine of healthy CAF members to minimize the risk of contraction and transmission of COVID-19. The physical distancing inherent to quarantine challenged Canadian Forces Health Services to adapt its health care delivery. CAF Physiotherapy responded by integrating telehealth to maintain provision of essential primary health care services. A modified After-Action Report was used to capture preliminary telehealth experiences of Defence Team physiotherapists since the activation of Operation LASER. To date, seven Physiotherapy Officers and six civilian physiotherapists, have delivered a total of 196 assessments lasting an average of 45 minutes, and a total of 765 follow-ups lasting an average of 25 minutes. Most respondents reported no previous experience or formal training providing telehealth. Most respondents reported delivering telehealth by telephone and acknowledged challenges, including non-standardized patient instructions, inadequate equipment, unsuitable environments, and limited patient feedback. To maximize the quality of telehealth delivery, respondents recommended standardizing patient instructions, establishing suitable work environments, and using telephone headsets, videoconferencing, and digital exercise software. These recommendations are an investment in the capacity of CAF Physiotherapy to maintain rehabilitation services in the post-COVID-19 environment.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".