Unmet Needs Among Older Adult Informal Caregivers and Care Recipients in Singapore: A Qualitative Study
Bibliographic record
Abstract
With the global population aging, it is imperative to have a thorough understanding of the unmet needs experienced by older adults who require caregiving or are informal caregivers. It is also important to understand how the perspectives of caregivers and care recipients might differ and interact to mutually shape experiences during the care journey. The primary aim of this study was to provide an in-depth and holistic understanding of the unmet needs and challenges experienced by older informal caregivers and care recipients. In-depth interviews were conducted in Singapore with 43 participants aged 60 years and above (35 caregivers and eight care recipients). Five main themes emerged from the analysis of the data: i) unmet needs due to informational gaps, ii) fear of burdening family members, iii) caregivers' de-prioritization of self-care due to care recipients' needs, iv) differing views between caregivers and care recipients, and v) concerns about the future. These findings highlight challenges that are especially pertinent to older informal caregivers and care recipients and suggest the need to improve support for them, including having more frequent check-ins, recalibrating policies and programs for more flexible and person-centered support, and facilitating more conversations between care recipients and caregivers about future caregiving arrangements. (198 words).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".