Practicing During the COVID-19 Pandemic: Experiences of Canadian Hospital-Based Occupational Therapists
Bibliographic record
Abstract
Background. The COVID-19 pandemic disrupted hospital operations worldwide, including services delivered by occupational therapists (OTs). Purpose. This study aimed to understand the experiences of OTs at one Canadian, tertiary care hospital during the 2021–2022 period of the pandemic. Method. We used a qualitative descriptive approach to describe the experiences of OTs during the pandemic. Findings. While there were similarities in the 10 participating OTs’ experiences, salient differences were largely linked to the method of service delivery. Inpatient OTs benefitted from the support of colleagues and developed coping strategies in response to high levels of workplace stress and anxiety and a perceived lack of support from many levels of society. Clinically, they spent more time on discharge planning with fewer resources. OTs providing virtual/hybrid services experienced unique challenges related to adapting their practice to a virtual platform, including challenges assessing patients. They described benefits associated with virtual/hybrid service delivery and brought up concerns around equity of service provision. Conclusion. OTs in this hospital setting faced challenges in providing patient care and supporting their own wellness during the pandemic. Future research could explore the role of leadership in supporting occupational therapy practice during public health emergencies.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".