FACILITATORS AND BARRIERS IN NAVIGATING COMMUNITY AND HEALTH CARE SYSTEM AMONG INFORMAL CAREGIVERS OF OLDER ADULTS
Bibliographic record
Abstract
Abstract Informal caregivers are fulfilling important support roles for older adults who tend to require a greater range of services while navigating these community and healthcare systems to fulfill their growing complex needs. Yet, there is sparse knowledge on the most significant facilitators and barriers to effective system navigation (SN) among caregivers. This paper aims to fill these gaps through an investigation of the key factors affecting SN difficulties among informal caregivers of older adults. The Behavioural-Ecological Framework of Healthcare Access and Navigation (BEAN) model is used to frame the study. Using the General Social Survey (GSS) on Caregiving and Care Receiving 2018, a total of 2,733 informal caregivers whose primary care receivers were aged 65 or older were analyzed. Hierarchical logistic regression showed that the probability of reporting SN difficulties was lower for caregivers with social capital/cohesion compared to those without social capital/cohesion. In comparison, the probability of reporting SN difficulties was higher among caregivers with caregiving supports and among caregivers whose care receivers use a higher amount of formal health services. Several sociodemographic covariates (e.g., sex, education level, self-rated stress) were also identified. Given the significance of the social capital and utilization factors, as well as several key covariates as facilitators/barriers in SN among informal caregivers, our findings support certain aspects of the BEAN model. To address the challenges and to fill the care gaps on SN among informal caregivers, there is a need to implement coordinated schemes and health policy for older adults and their 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".