Factors associated with mental health outcomes among family caregivers to adults with COVID: a scoping review
Bibliographic record
Abstract
PURPOSE: Family caregivers (FCGs) are essential to the health and wellbeing of people affected by COVID. Protecting mental health of FCGs is essential to sustaining their caregiving role. The objective of this scoping review was to synthesise identified risks factors and protective factors for mental health of FCGs to adults with COVID. MATERIALS AND METHODS: Using the Joanna Briggs Institute (JBI) methodology, the search was conducted across Medline, CINAHL, and PsycINFO. Original studies conducted since the pandemic began were included. The population was adult FCGs to adults with COVID, and studies reported mental health outcomes and related factors. RESULTS: Of 3474 identified articles, 22 met inclusion criteria (14 quantitative, seven qualitative, one mixed-methods, 18/22 conducted in Iran). Across all study designs, risk factors included limited support, financial burden, family challenges, unpredictable nature of COVID, inexperience, isolation, and unpleasant experiences. Protective factors included accessing support services, self-reinforcement, coping strategies, professional help, and online intervention. CONCLUSIONS: Quantitative and qualitative research identified common mental health risk factors and protective factors for FCGs to adults with COVID. These factors may inform development of supports and services for FCGs to people with COVID, such as online interventions. Studies did not distinguish acute versus long COVID.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".