Cancer patient perspectives during the COVID-19 pandemic: A thematic analysis of cancer blog posts
Bibliographic record
Abstract
The content of online cancer patient blogs has previously been analyzed to inform physicians about the cancer experience and patient concerns. The coronavirus disease 2019 (COVID-19) pandemic has greatly affected cancer patients due to their vulnerable health status, as well as changes in cancer testing and treatment. We sought to qualitatively describe the concerns and experiences expressed online by cancer patients, survivors, and family members in relation to COVID-19. 152 blog posts written by cancer patients, survivors, or family members, were selected using combined Boolean searches and snowball sampling. Reviewers extracted subthemes from blog posts using line-by-line text analysis until a sufficient sample was achieved. Subthemes were hierarchically organized into major theme categories and illustrative quotations were identified. A total of 80 blog posts posted between January 20th and April 6th, 2020 were analyzed, revealing 23 subthemes. Major theme categories included: the direct and indirect impacts of COVID-19 on personal health and the health of others, comparisons between COVID-19 and the cancer experience, the impact of COVID-19 on social and psychological wellbeing, perspectives on government and the public response to COVID-19, and coping mechanisms and gratitude. COVID-19 has significantly affected cancer patients, survivors, and family members. Subthemes and quotations relating to perceived medical abandonment, patient mental health, and the impact of previous cancer trauma on the ability to cope with COVID-19 highlight the need for healthcare professionals to be cognizant of evolving patient concerns, so they may provide reassurance and appropriate care to their patients in these exceptional circumstances. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework. (http://bit.ly/ExperienceFramework) Access other PXJ articles related to this lens. Access other resources related to this lens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".