The Perceived Impact of COVID-19 on the Mental Health Status of Adolescent and Young Adult Survivors of Childhood Cancer and the Development of a Knowledge Translation Tool to Support Their Information Needs
Bibliographic record
Abstract
Background: Adolescent and young adult (AYA; 13 to 39 years) survivors of childhood cancer may be especially vulnerable to physical health and mental health concerns during the pandemic. We investigated the impact of COVID-19 on the mental health status of AYA survivors (Aim 1) and shared tailored, evidence-based health-related information on COVID-19 (Aim 2). Methods: Between May and June 2020, participants completed a cross-sectional online survey assessing their cancer history, current mental health status, and their COVID-19 information needs. Results: Ninety-four participants (78 females, 13 males, 2 non-binary) with a mean age of 26.9 years (SD = 6.2) were included in the final sample. Participants reported residing from 10 countries and 94% identified as White. Nearly half of the participants (49%) described their mental health status as worse now than before the pandemic. Thirty-nine participants (41%) that indicated their current mental health status was tied to fears/worries about their past cancer and treatment experienced a higher level of anxiety and PTSS than those who did not report the same. Most participants (77%) had not received any information related to the potential risks of COVID-19 and expressed an interest in receiving this information. In response, an infographic detailing recommended strategies for coping with mental health problems in the pandemic, along with preliminary study findings, was developed. Discussion: AYA survivors reporting their mental health status was linked to their past cancer experienced poorer mental health. There is a value to educating survivors on their potential health risks, but accounting for their perceived mental health vulnerabilities should be considered when disseminating knowledge. The use of an infographic is a unique contribution towards the development of innovative and personalized means of sharing health education to this vulnerable yet resilient group. This research on the mental health status of AYA survivors very early in the pandemic informs continued initiatives investigating the rapidly changing nature of how COVID-19 may impact AYA survivors today and in the future.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".