Healthcare system navigation difficulties among informal caregivers of older adults: a logistic regression analysis of social capital, caregiving support and utilization factors
Bibliographic record
Abstract
BACKGROUND: Informal caregivers of older adults play a vital role in improving the degree to which older adults access community and healthcare services in a seamless and timely manner. They are fulfilling important navigation and support roles for their older care recipients. However, there is still little knowledge of the most significant facilitators and barriers to effective and efficient system navigation among caregivers. This paper aims to fill these knowledge gaps through investigation of the key factors (i.e., social capital/cohesion, caregiving supports, and utilization factors) affecting navigation difficulties faced by informal caregivers of older adults. METHODS: The Behavioural-Ecological Framework of Healthcare Access and Navigation (BEAN) model is used to frame the study. Using the General Social Survey on Caregiving and Care Receiving 2018, we analyzed 2,733 informal caregivers whose primary care recipients were aged 65 or older. Hierarchical logistic regression was conducted to identify the relationship between system navigation difficulties among informal caregivers and four sequentially ordered blocks of predictors: (1) sociodemographic (2), social capital/cohesion (3), caregiving supports, and (4) healthcare demand. RESULTS: The fully adjusted model showed that the probability of reporting navigation difficulties was lower for caregivers with social capital/cohesion compared to those without social capital/cohesion. In comparison, the probability of reporting navigation difficulties was higher among caregivers with caregiving support and among caregivers whose care receivers use a higher amount of health service use. Several sociodemographic covariates were also identified. CONCLUSION: Our findings support certain aspects of the BEAN model. This study extends our understanding of potential facilitators and barriers that informal caregivers of older adults face while navigating complex community and health systems. There is a need to implement coordinated schemes and health policies especially for older adults with mental/neurological issues to address the challenges of their caregivers given the specific vulnerability identified in this study. The need for further research using different approaches to examine the disproportionate impact of COVID-19 on caregivers' system navigation experience is crucial.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".