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Record W3211921961 · doi:10.2196/28895

Exploring the Experiences of Family Caregivers of Children With Special Health Care Needs to Inform the Design of Digital Health Systems: Formative Qualitative Study

2021· article· en· W3211921961 on OpenAlexafffundvenue
Ryan Tennant, Sana Allana, Kate Mercer, Catherine M. Burns

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsThematic analysisHealth careFamily caregiversNursingFormative assessmentQualitative researchMedicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Family caregivers of children with special health care needs (CSHCN) are responsible for managing and communicating information regarding their child's health in their homes. Although family caregivers currently capture information through nondigital methods, digital health care applications are a promising solution for supporting the standardization of information management in complex home care across their child's health care team. However, family caregivers continue to use paper-based methods where the adoption of digital health care tools is low. With the rise in home care for children with complex health care needs, it is important to understand the caregiving work domain to inform the design of technologies that support child safety in the home. OBJECTIVE: The aim of this study is to explore how family caregivers navigate information management and communication in complex home care for CSHCN. METHODS: This research is part of a broader study to explore caregivers' perspectives on integrating and designing digital health care tools for complex home care. The broader study included interviews and surveys about designing a voice user interface to support home care. This formative study explored semistructured interview data with family caregivers of CSHCN about their home care situations. Inductive thematic analysis was used to analyze the information management and communication processes. RESULTS: We collected data from 7 family caregivers in North America and identified 5 themes. First, family caregivers were continuously learning to provide care. They were also updating the caregiver team on their child's status and teaching caregivers about their care situation. As caregiving teams grew, they found themselves working on communicating with their children's educators. Beyond the scope of managing their child's health information, family caregivers also navigated bureaucratic processes for their child's home care. CONCLUSIONS: Family caregivers' experiences of caring for CSHCN differ contextually and evolve as their child's condition changes and they grow toward adulthood. Family caregivers recorded information using paper-based tools, which did not sufficiently support information management. They also experienced significant pressure in summarizing information and coordinating 2-way communication about the details of their child's health with caregivers. The design of digital health care systems and tools for complex home care may improve care coordination if they provide an intuitive method for information interaction and significant utility by delivering situation-specific insights and adapting to unique and dynamic home care environments. Although these findings provide a foundational understanding, there is an opportunity for further research to generalize the findings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0140.010
Scholarly communication0.0040.005
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.301
GPT teacher head0.551
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2021
Admission routes3
Has abstractyes

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