The psychological burden of waiting for procedures and patient‐centred strategies that could support the mental health of wait‐listed patients and caregivers during the COVID‐19 pandemic: A scoping review
Bibliographic record
Abstract
BACKGROUND: Waiting for procedures delayed by COVID-19 may cause anxiety and related adverse consequences. OBJECTIVE: To synthesize research on the mental health impact of waiting and patient-centred mitigation strategies that could be applied in the COVID-19 context. METHODS: Using a scoping review approach, we searched 9 databases for studies on waiting lists and mental health and reported study characteristics, impacts and intervention attributes and outcomes. RESULTS: We included 51 studies that focussed on organ transplant (60.8%), surgery (21.6%) or cancer management (13.7%). Most patients and caregivers reported anxiety, depression and poor quality of life, which deteriorated with increasing wait time. The impact of waiting on mental health was greater among women and new immigrants, and those of younger age, lower socio-economic status, or with less-positive coping ability. Six studies evaluated educational strategies to develop coping skills: 2 reduced depression (2 did not), 1 reduced anxiety (2 did not) and 2 improved quality of life (2 did not). In contrast, patients desired acknowledgement of concerns, peer support, and periodic communication about wait-list position, prioritization criteria and anticipated procedure date. CONCLUSIONS: Findings revealed patient-centred strategies to alleviate the mental health impact of waiting for procedures. Ongoing research should explore how to optimize the impact of those strategies for diverse patients and caregivers, particularly in the COVID-19 context. PATIENT OR PUBLIC CONTRIBUTION: Six patients and four caregivers waiting for COVID-19-delayed procedures helped to establish eligibility criteria, plan data extraction and review a draft and final report.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".