Health Services: A Mixed Methods Assessment of Canadian Cancer Patient Education Materials Related to the 2019 Novel Coronavirus
Bibliographic record
Abstract
The 2019 novel coronavirus (COVID-19) pandemic has prompted the reorganization in the scheduling and method of care for many patients, including patients diagnosed with cancer. Cancer patients, who have an immunocompromised status, may be at a higher risk of severe symptoms from infection with COVID-19. While information is rapidly evolving regarding COVID-19, Canada, both nationally and provincially, has been conveying new information to patients online. We assessed the content and readability of COVID-19-related online Canadian patient education material (PEM) for cancer patients to determine if the content of the material was written at a grade reading level that the majority of Canadians can understand. PEMs were extracted from provincial cancer agencies and the national Canadian Cancer Society, evaluated using 10 readability scales, qualitatively analyzed to identify their themes and difficult word content. Thirty-eight PEMs from both national and provincial cancers associations were, on average, written above the recommended 7th grade level. Each of the associations’ average grade levels were: BC Cancer (11.00 95% confidence interval [CI] 8.27-13.38), CancerControl Alberta (10.46 95% CI 8.29-12.62), Saskatchewan Cancer Agency (11.08 95% CI 9.37-12.80), Cancer Care Manitoba (9.55 95% CI 6.02-13.01), Cancer Care Ontario (9.35 95% CI 6.80-11.90), Cancer Care Nova Scotia (10.95 95% CI 9.86-12.04), Cancer Care Eastern Health Newfoundland and Labrador (10.14 95% CI 6.87-13.41), and the Canadian Cancer Society (10.06 95% CI 8.07-12.05). Thematic analysis identified 4 themes: public health strategy, information about COVID-19, patient instructions during COVID-19, and resources. Fifty-three percent of the complex words identified were medical jargon. This represents an opportunity to improve PEM readability, to allow for greater comprehension amongst a wider target audience.
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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 | 0.034 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".