Continued Implementation and Use of a Digital Informal Care Support Platform Before and After COVID-19: Multimethod Study
Bibliographic record
Abstract
Background: With the growing need of support for informal caregivers (ICs) and care recipients (CRs) during COVID-19, the uptake of digital care collaboration platforms such as Caren increased. Caren is a platform designed to (1) improve communication and coordination between ICs and health care professionals, (2) provide a better overview of the care process, and (3) enhance safe information sharing within the care network. Insights on the impact of COVID-19 on the implementation and use of informal care platforms such as Caren are still lacking. Objective: This study aimed to (1) identify technology developers' lessons learned from the continued implementation of Caren during COVID-19 and (2) examine pre-post COVID-19 changes in usage behavior and support functionality use of Caren. Methods: A focus group with developers of the Caren platform (N=3) was conducted to extract implementation lessons learned. Focus group data were first analyzed deductively, using the Consolidated Framework for Implementation Research domains (ie, individual characteristics, intervention characteristics, inner setting, and outer setting). Later, inductive analysis of overarching themes was performed. Furthermore, survey data were collected in 2019 (N=11,635) and 2022 (N=5573) among Caren platform users for comparing usage behavior and support functionality use. Data were analyzed using descriptive and inferential statistics. Results: Several lessons from the continued implementation of Caren during COVID-19 were identified. Those included, for example, alternative ways to engage with end users, incorporating automated user support and large-scale communication features, considering the fluctuation of user groups, and addressing data transparency concerns in health care. Quantitative results showed that the number of ICs and CRs who used Caren several times per day increased significantly (P<.001 for ICs and CRs) between 2019 (ICs: 23.8%; CRs: 23.2%) and 2022 (ICs: 35.2%; CRs: 37%), as well as the use of certain support functionalities such as a digital agenda to make and view appointments, a messaging function to receive updates and communicate with formal and informal caregivers, and digital notes to store important information. Conclusions: Our study offers insights into the influence of the COVID-19 pandemic on the usage and implementation of the digital informal care support platform Caren. The study shows how platform developers maintained the implementation during COVID-19 and which support functionalities gained relevance among ICs and CRs throughout the pandemic. The findings can be used to improve the design and implementation of current and future digital platforms to support informal care toward the "new digital normal."
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".