The Implementation Outcomes and Population Impact of a Statewide IT Deployment for Family Caregivers: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: In 2022, the US Department of Health and Human Services released the first National Strategy to Support Family Caregivers, identifying actions for both government and the private sector. One of the major goals is to expand data, research, and evidence-based practices to support family caregivers. While IT tools are widely deployed in health care settings, they are rarely available at scale in community agencies. In 2019, the state of California recognized the importance of a statewide database and a platform to serve caregivers remotely by enhancing existing service supports and investing in a web-based platform, CareNav. Implementation commenced in early 2020 across all 11 California Caregiver Resource Centers. OBJECTIVE: This paper describes the implementation strategies and outcomes of the statewide implementation of CareNav, a web-based platform to support family caregivers. METHODS: The Consolidated Framework for Implementation Research (CFIR), including a recent addendum, guided this mixed methods evaluation. Two major approaches were used to evaluate the implementation process: in-depth qualitative interviews with key informants (n=82) and surveys of staff members (n=112) and caregivers (n=2229). We analyzed the interview transcripts using qualitative descriptive methods; subsequently, we identified subthemes and relationships among the ideas, mapping the findings to the CFIR addendum. For the surveys, we used descriptive statistics. RESULTS: We present our findings about implementation strategies, implementation outcomes (ie, adoption, fidelity, and sustainment), and the impact on population health (organizational effectiveness and equity, as well as caregiver satisfaction, health, and well-being). The platform was fully adopted within 18 months, and the system is advancing toward sustainment through statewide collaboration. The deployment has augmented organizational effectiveness and quality, enhanced equity, and improved caregiver health and well-being. CONCLUSIONS: This study provides a use case for technological implementation across a multisite system with diverse community-based agencies. Future research can expand the understanding of the barriers and facilitators to achieving relevant outcomes and population impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".