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Record W3197813711 · doi:10.1177/23743735211039328

Cancer Treatment During COVID-19: A Qualitative Analysis of Patient-Perceived Risks and Experiences with Virtual Care

2021· article· en· W3197813711 on OpenAlexafffundabout
Lesley Gotlib Conn, Houman Tahmasebi, Nicholas Meti, Frances C. Wright, Alia Thawer, Matthew C. Cheung, Simron Singh

Bibliographic record

VenueJournal of Patient Experience · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsThematic analysisFocus groupQualitative researchMedicineAnxietyCancerPandemicFamily medicineNursingCoronavirus disease 2019 (COVID-19)PreferencePsychologyPsychiatryDiseasePathology

Abstract

fetched live from OpenAlex

During the COVID-19 pandemic, most cancer centers shifted from in-person to virtual cancer care to curb community spread and ensure care continuity. This qualitative descriptive study aimed to understand cancer patient-perceived risks related to COVID-19 and cancer treatment, as well as the patient-perceived and experienced value of virtual care. From June to August 2020, focus groups were conducted with patients under active management or observation for a diagnosed malignancy in Toronto, Canada. A thematic analysis of six focus groups found that most participants worried more about treatment delays than they did about COVID-19 infection. Despite some concern about COVID-19 exposure in the hospital, care delays contributed to increased anxiety among participants who already subscribed to strict safety measures in their everyday lives. Most participants accepted virtual care for some appointment types; however, preference for in-person care was found to sustain the humanistic and therapeutic aspects of cancer care that many participants valued. Nuances in the appropriateness and adequacy of virtual cancer care still need exploration. Preserving the humanistic aspects of care is of paramount importance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.026
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.093
GPT teacher head0.466
Teacher spread0.373 · 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 teacher head, 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".

Quick stats

Citations30
Published2021
Admission routes3
Has abstractyes

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