Implementation of a Statewide Web-Based Caregiver Resource Information System (CareNav): Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: With the aging population, family caregivers provide increasingly complex and intense care for older adults and persons with disabilities. There is growing interest in developing community-based services to support family caregivers. Caregiving occurs around the clock, and caregivers face challenges in accessing community-based services at convenient times owing to the demands of care. Web-based resources hold promise for accessible real-time support. CareNav (TM), a caregiver resource information system, is a web-based platform designed to support real-time universal caregiver assessment, a record of client encounters, development of a care plan, tailored information and resource content, access to web-based caregiver resources, the capacity to track service authorization and contracts, and secure communications. The assessment includes needs and health conditions of both the care recipient and caregiver; current resources; and priorities for support, information, and referral. In 2019, the California Department of Health Care Services funded the 11 nonprofit California Caregiver Resource Centers (CRCs) to expand and improve family caregiver services and enhance CRC information technology services. Deployment of a statewide information system offered a unique opportunity to examine structures and processes facilitating implementation, providing feedback to the sites as well as lessons learned for similar projects in the future. OBJECTIVE: The aim of this paper was to describe the statewide implementation of the comprehensive CareNav system using the Consolidated Framework for Implementation Research as an organizing structure for synthesizing the evaluation. METHODS: This mixed methods study used two major approaches to evaluate the implementation process: a survey of all staff who completed training (n=82) and in-depth qualitative interviews with 11 CRC teams and 3 key informants (n=35). We initially analyzed interview transcripts using qualitative descriptive methods and then identified subthemes and relationships among ideas, mapping the findings to the Consolidated Framework for Implementation Research. RESULTS: We present findings on the outer setting, inner setting, characteristics of the intervention, characteristics of the staff, and the implementation process. The critical elements for success were leadership, communication, harmonization of processes across sites, and motivation to serve clients in more accessible and convenient ways. CONCLUSIONS: These findings have implications for technology deployment in diverse community-based agencies that aspire to enhance web-based services.
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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 | 0.037 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".