Enlightening Stakeholders on the COVID‐19 Pandemic Impacts and Preparation for Minimizing Future Pandemics’ Negative Effects on Occupational Therapists
Bibliographic record
Abstract
The COVID‐19 pandemic has affected all aspects of professional disciplines including occupational therapy; however, little is known about how much of an impact the COVID‐19 pandemic affected occupational therapy practice in Ghana. This study examined the impacts, coping strategies, and COVID‐19 pandemic lessons for occupational therapy practices in the future. A descriptive qualitative design was employed with a purposive and convenience sampling methods to recruit occupational therapists from four practice settings in Ghana. Semi‐structured interviews were conducted with seven participants. Interviews were audio‐recorded, transcribed, and analyzed using thematic analysis. Four major themes were identified from the analysis enlightening stakeholders on the impact and preparation for minimizing the impact of future pandemics on the workloads of an occupational therapist. The major themes are (1) impacts of the COVID‐19 on occupational therapy practice and practitioners; (2) aspects or domains of work significantly affected by the pandemic; (3) the existing strategies employed to handle the challenges; and (4) strategies to minimize these challenges in the future. The current study has enlightened stakeholders on the need to make alternate preparations including telehealth, continuous support for telehealth infrastructures, training of practitioners, and research to adapt intervention strategies effectively.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".