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Record W2753387743 · doi:10.1111/petr.13049

Talking with caregivers of children living in the community with ventricular assist devices

2017· article· en· W2753387743 on OpenAlexafffund
Courtney R. Petruik, Cheryl Mack, Jennifer Conway, Holger Buchholz, Michael van Manen

Bibliographic record

VenuePediatric Transplantation · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of Alberta
FundersWomen and Children's Health Research Institute
KeywordsMedicineQualitative researchNegotiationKinshipVentricular assist deviceNursingHeart failure

Abstract

fetched live from OpenAlex

A VAD is a mechanical pump used to support the functioning of a failing heart. As a pediatric therapy, a VAD is used as a temporary solution for poor heart function, a bridge to transplantation or recovery, or a destination therapy. The goal of this qualitative study was to explore the perspectives of family and professional caregivers of children who are supported by VADs in outpatient settings. Semi-structured interviews were conducted with 22 caregivers of school-aged children discharged home on VAD support. Interviews were transcribed, and data were analyzed using qualitative content analysis. Caregivers identified issues facing children on VAD support in the contexts of home, school, and other childhood places including being physically connected to a device; experiencing changes; living a medical life; negotiating restrictions; cost of care; family, kinship, and community; and, present and future living. While a child with a VAD may have much in common with other medically complex children, the technological complications and risks of living with a VAD are uniquely identified by caregivers as an issue, especially when considering the way that children with a VAD are connected to their device-implanted yet exterior, mobile yet restricted, and autonomous yet dependent.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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 designObservational
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

Citations14
Published2017
Admission routes2
Has abstractyes

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