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Impact of the COVID-19 pandemic on patient perceptions of the quality of their cancer care.

2022· article· en· W4298139448 on OpenAlexaffabout
Melanie Powis, Shabbir M.H. Alibhai, Alejandro Berlín, Rinku Sutradhar, Simron Singh, Saidah Hack, Monika K. Krzyzanowska

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicinePandemicFamily medicineLogistic regressionCancerCoronavirus disease 2019 (COVID-19)Health careDiseaseInternal medicine

Abstract

fetched live from OpenAlex

286 Background: COVID has had a significant impact on how cancer care is delivered. Numerous modifications to care aimed at reducing in-person visits, and mitigating potential issues with staff and resource shortages were introduced, including the implementation of virtual visits. As many of the changes to cancer care are likely to persist beyond the pandemic, we undertook a survey to understand patients’ perceptions of the impact of the pandemic on the quality of cancer care that they received. Methods: We developed an electronic survey guided by the Picker Institute’s eight principles of patient-centred care. Patients >18 years of age with a valid email address on file who have had a visit in the gastrointestinal clinic at Princess Margaret Cancer Centre (PM) in Toronto, Canada between 03/2020 and 05/2021 were recruited via email invitation in 10/2021 to anonymously participate. Logistic regression models were utilized to examine adjusted associations between demographic and clinical variables, and reporting a negative perception of care. Results: The response rate was 17.1% (438/2563); after excluding responses from those who are no longer patients and incomplete responses, 358 responses were included in the final analysis. Respondents were most commonly >65 years of age (46.6%), male (48.6%), college/university educated (55.5%) and in follow-up phase of their disease (54.2%). Approximately half of respondents (56.1%) had some experience with care at PM prior to the pandemic, while the remaining patients were referred during. Most respondents (76.3%) indicated that, overall, they were satisfied with the care they received during COVID. Compared to patients who experienced care at PM prior to the pandemic, patients referred during COVID were 2.5 times more likely to believe that patients received worse care during COVID than those treated prior (OR: 2.42; 95% CI: 1.08-5.44). Most respondents (69.5%) reported that virtual appointments positively impacted the quality of their cancer care. Adequacy of emotional support was the most negatively perceived dimension of patient-centred care, whereby 18.7% of patients felt they were provided with inadequate information on support services available to them, and 17.6% felt that they had received inadequate support. Relative to those in follow-up, patients with a newly diagnosed cancer were more likely to report receipt of inadequate emotional support (OR: 5.69; 95% CI: 2.18-14.82). Conclusions: Most patients reported being satisfied with the quality of care provided to them during COVID and reported a positive impact of virtual appointments on their care. Future quality improvement efforts should focus on how to improve dissemination of information on available support services and access to appropriate emotional supports, particularly for newly diagnosed patients.

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)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.470
GPT teacher head0.644
Teacher spread0.174 · 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 designObservational
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 routes2
Has abstractyes

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