HOW DOES MODE OF SERVICE DELIVERY IMPACT CAREGIVER STRAIN? A MIXED-METHODS STUDY OF CAREGIVERS IN LOS ANGELES
Bibliographic record
Abstract
Abstract As the number of people living with Alzheimer’s disease and related dementias increases, so too will the number of people who care for them. This growing population requires instrumental and emotional support as they fulfill their caregiving duties. CareJourney is an online platform that provides this support; it can be used either alone, or with traditional in-person and phone-mediated consultations with family care navigators (FCNs). The intent of CareJourney is to provide a greater opportunity for self-directed services, as well as flexibility for working caregivers and those who prefer using technology. Satisfaction surveys (N=222) were analyzed to evaluate caregivers’ perceptions of CareJourney and the traditional service delivery modes. Additionally, five 30-minute interviews with caregivers were coded by two researchers (kappa=0.84) for a more in-depth understanding. Ten percent of survey respondents used CareJourney, and preliminary results from both the interviews and the survey suggest that caregiver burden, lack of time, and desire for a personal connection with FCNs influence mode of service delivery. Although a quarter of users relied on CareJourney to find caregiving resources and email their FCNs because it was more convenient than traditional modes, the interviews and surveys revealed that the caregivers most valued the personal touch that came from interacting with FCNs. Speaking with FCNs over the phone or in-person allowed caregivers to feel “reassured,” “less isolated,” and “comforted” as they discussed caregiving strains. Future interventions and services for caregivers should be offered using multiple modes to accommodate a range of time-demands and preferences for personal connection.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".