Impact of coronavirus of 2019 on the delivery of pharmacy services to patients with cancer: An international survey of oncology pharmacy practitioners
Bibliographic record
Abstract
INTRODUCTION: The coronavirus of 2019 pandemic has necessitated vast and rapid changes in the way oncology pharmacy services are delivered around the world. METHODS/AIMS: An international survey of oncology pharmacists and technicians was conducted via the International Society of Oncology Pharmacy Practitioners and collaborating global pharmacy organisations to determine the impact that the coronavirus of 2019 has had on pharmacy service delivery, pharmacy practitioners and oncology practice. RESULTS: = 841, 97.6%), with 24% involved in the direct care of patients with the coronavirus of 2019. Of the survey participants, 55% increased their time working remotely, with remote activities including dispensing, patient assessment/follow-up and attending multi-disciplinary rounds. Respondents reported a 72% increase in the use of technology to perform remote patient interaction activities and that participation in educational meetings and quality improvement projects was reduced by 68% and 44%, respectively. Workforce impacts included altered working hours (50%), cancelled leave (48%) and forced leave/furloughing (30%). During the pandemic, respondents reported reduced access to intensive care (19%) and anti-cancer (15%) medications. In addition, 39% of respondents reported reduced access to personal protective equipment, including N95 masks for chemotherapy compounding. Almost half of respondents (49%) reported that cancer treatments were delayed or intervals were altered for patients being treated with curative intent. A third of practitioners (30%) believed that patient outcomes would be adversely impacted by changes to pharmacy services. Sixty-five percent of respondents reported impacts on their mental health, with 12% utilising support services. CONCLUSION: The coronavirus of 2019 pandemic has altered the way oncology pharmacy services are delivered. These results demonstrate the adaptability of the oncology pharmacy profession and highlight the importance of formal evaluation of the varied practice models to determine the evidence-based practices that enhance pharmacy services and, thus, should be reinstated as soon as practical and reasonable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".