Caregivers to older adults require support: A scoping review of their priorities
Bibliographic record
Abstract
The vast majority of older adults who are chronically ill rely on informal caregivers for support. Caregivers often require additional support to facilitate their role. To the best of our knowledge, there has yet to be a collation of caregiver-identified priorities for support. Using existing research, this scoping review provides a comprehensive picture of what caregivers have indicated as priorities for support. Arksey and O'Malley's scoping review framework guides this review. We searched MEDLINE, CINAHL and PsycINFO databases on July 2, 2021. We selected databases based on their relevance to nursing, health and social science. Inclusion criteria were peer-reviewed research of any design, a sample population of caregivers to older adults (>55 years), manuscripts published in English and the priorities for caregiver support identified by caregivers themselves. We screened a total of 3591 records, and 33 articles met the inclusion criteria. These studies were from geographic settings across the globe and used various quantitative, qualitative and mixed-method study designs. In our synthesis, we quantified the identified priorities within the studies using coding and content analysis. We present the following list of caregiver-identified priorities: (1) orientation to the caregiving role; (2) self-care and respite; (3) adapting healthcare; (4) improved supports; (5) information needs; (6) access to resources; (7) financial assistance. Policymakers, healthcare professionals and non-profit organisations can use evidence from this review to guide decisions when developing support services and interventions for caregivers.
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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 | 0.035 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".