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Record W4382130544 · doi:10.2196/41185

Uptake and Use of Care Companion, a Web-Based Information Resource for Supporting Informal Carers of Older People: Mixed Methods Study

2023· article· en· W4382130544 on OpenAlexvenueno aff
Jeremy Dale, Veronica Nanton, Theresa Day, Patricia Apenteng, Celia J. Bernstein, Gillian Grason Smith, Peter Strong, Rob Procter

Bibliographic record

VenueJMIR Aging · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
FundersWarwick Medical SchoolUniversity of WarwickDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsFeelingResource (disambiguation)NursingPsychologyHealth carePopulationQualitative propertyPsychological resilienceQualitative researchMedicineSocial psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Informal carers play a major role in supporting relatives and friends who are sick, disabled, or frail. Access to information, guidance, and support that are relevant to the lives and circumstances of carers is critical to carers feeling supported in their role. When unmet, this need is known to adversely affect carer resilience and well-being. To address this problem, Care Companion was co-designed with current and former carers and stakeholders as a free-to-use, web-based resource to provide access to a broad range of tailored information, including links to local and national resources. OBJECTIVE: This study aimed to investigate the real-world uptake and use of Care Companion in 1 region of England (with known carer population of approximately 100,000), with local health, community, and social care teams being asked to actively promote its use. METHODS: The study had a convergent parallel, mixed methods design and drew on the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. Data included metrics from carers' use of Care Companion, surveys completed by users recruited through general practice, and interviews with carers and health and social care providers regarding their views about Care Companion and their response to it. Quantitative data were analyzed using descriptive statistics. Interview data were analyzed thematically and synthesized to create overarching themes. The qualitative findings were used for in-depth exploration and interpretation of quantitative results. RESULTS: Despite awareness-raising activities by relevant health, social care, and community organizations, there was limited uptake with only 556 carers (0.87% of the known carer population of 100,000) registering to use Care Companion in total, with median of 2 (mean 7.2; mode 2) visits per registered user. Interviews with carers (n=29) and stakeholders (n=12) identified 7 key themes that influenced registration, use, and perceived value: stakeholders' signposting of carers to Care Companion, expectations about Care Companion, activity levels and conflicting priorities, experience of using Care Companion, relevance to personal circumstances, social isolation and networks, and experience with digital technology. Although many interviewed carers felt that it was potentially useful, few considered it as being of direct relevance to their own circumstances. For some, concerns about social isolation and lack of hands-on support were more pressing issues than the need for information. CONCLUSIONS: The gap between the enthusiastic views expressed by carers during Care Companion's co-design and the subsequent low level of uptake and user experience observed in this evaluation suggests that the co-design process may have lacked a sufficiently diverse set of viewpoints. Numerous factors were identified as contributing to Care Companion's level of use, some of which might have been anticipated during its co-design. More emphasis on the development and implementation, including continuing co-design support after deployment, may have supported increased use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.357
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

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