The Psychosocial Impact of COVID-19 on Older Adults with Cancer: A Rapid Review
Bibliographic record
Abstract
BACKGROUND: Older adults with cancer are amongst the most vulnerable population to be negatively impacted by COVID-19 due to their likelihood of comorbidities and compromised immune status. Considering the longevity of the pandemic, understanding the subjective perceptions and psychosocial concerns of this population may help ameliorate the psychological aftermath. In this review, we systematically analyze the literature surrounding the psychosocial impact and coping strategies among older adults with cancer within the context of COVID-19. METHODS: We conducted a rapid review of literature following PRISMA guidelines between January 2020 to August 2021 using (1) MEDLINE, (2) Embase, (3) CINAHL, and (4) PsychINFO and keyword searches for "cancer" and "COVID-19" focused on adults 65 years or older. RESULTS: Of the 6597 articles screened, 10 met the inclusion criteria. Based on the included articles, the psychosocial impact of COVID-19 was reported under four domains, (1) impact of COVID-19 on quality of life (QoL), (2) concerns related to COVID-19, (3) coping with the impact of COVID-19, and (4) recommendations for future care. Results pertaining to perceived quality of life were inconsistent across the included articles. The most common concerns related to: contracting COVID-19, survivorship transitions, and feelings of isolation. Coping strategies reported by older adults included: spiritual care, lived experience, acceptance, and positive reinterpretation. CONCLUSIONS: We found many psychosocial impacts of the pandemic on older adults with cancer. The findings from this review can inform interventions related to shared decision-making and tailored patient care 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".