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Staff experiences with remote work in a comprehensive cancer center during the COVID-19 pandemic.

2022· article· en· W4298139915 on OpenAlexafffundabout
Christopher McChesney, Melanie Powis, Osvaldo Espin‐Garcia, Lyndon Morley, Saidah Hack, Monika K. Krzyzanowska

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersPrincess Margaret Cancer Foundation
KeywordsRespondentMedicinePersonal protective equipmentDescriptive statisticsPandemicHealth careFamily medicineLogistic regressionNursingWork (physics)Patient safetyCoronavirus disease 2019 (COVID-19)Medical emergencyDisease

Abstract

fetched live from OpenAlex

41 Background: The COVID-19 pandemic led to the rapid implementation of remote work, but few studies have evaluated the impact on staff and care delivery in healthcare settings. Utilizing a cross-sectional survey, we evaluated the experience of working remotely among clinical staff at the Princess Margaret Cancer Centre, Toronto, Canada. Methods: “Remote work” was defined as any work (tasks, projects, healthcare delivery) performed from home. A Qualtrics survey was disseminated via email three times from June-August 2021 to 1,168 physicians, nurses, allied health, and administrative staff involved in patient care. The survey evaluated staff perceptions of productivity, efficiency, patient safety, quality, and personal experiences. Results were summarized using descriptive statistics. Associations between respondent demographics and responses on perceived efficiency, desire to work remotely, support and safety/quality were evaluated using multivariable binary logistic regression models. Free-text responses were categorized into facilitators and barriers, and summarized using qualitative descriptive analysis. Results: Most respondents (n = 333; response rate: 28.5%) were female (61.3%) and physicians (23.1%). Few respondents (1.5%) worked remotely more than half the time pre-COVID which increased to 66.6% during COVID-19. Majority reported that remote work positively impacted productivity (61.8%) and efficiency (57.6%) and expressed interest in continuing (79.0%) beyond the pandemic. While most respondents agreed with the switch to remote work (89.2%), few were provided with the necessary equipment (14.1%). Some respondents perceived a negative impact on the safety (13.8%) or quality of care (18.6%) delivered remotely. Across clinical roles, compared to administrative staff, physicians were more likely to report remote work having a negative impact on productivity (OR = 24.04, 95% CI: 2.71-213.00), being dissatisfied with remote work (OR: 8.41, 95% CI: 1.37-51.64), being dissatisfied with training available (OR: 2.70, 95% CI: 0.95-7.67), and disagreed with continuing to utilize remote work beyond the pandemic (OR: 16.61, 95% CI: 1.45-190.14). Improved efficiency, less commuting, and improved work-life balance were perceived as facilitators of remote work. Barriers included a lack of clear role expectations, issues accessing clinical applications and out-of-pocket expenses. Conclusions: Our findings indicate that remote work may be a viable model in hospitals beyond the pandemic. Given the variation in experience and perceptions depending on clinical role, addressing barriers and formal evaluation of the impact of working remotely on productivity, efficiency and quality of care should be considered to inform long-term adoption.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.266
GPT teacher head0.548
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes3
Has abstractyes

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