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Emotional experiences of caregivers of patients with a ventricular assist device

2010· article· en· W2085861805 on OpenAlexaff
Annemarie Kaan, Quincy‐Robyn Young, Sarah J. Cockell, Martha Mackay

Bibliographic record

VenueProgress in Transplantation · 2010
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMedicineNursingFamily medicine

Abstract

fetched live from OpenAlex

CONTEXT: Little is known about the stresses experienced by caregivers of patients discharged home with a ventricular assist device. OBJECTIVE: To describe the lived experience of caregivers of patients who were discharged home with a ventricular assist device. DESIGN: The study used a phenomenological framework to conduct semistructured interviews guided by 2 psychologists using a focus group setting. PARTICIPANTS: Interviews of 13 caregivers of 9 patients discharged to home with a ventricular assist device between March 2004 and June 2007 were recorded, transcribed, and analyzed. RESULTS: Four themes emerged during the interviews: anxiety, initially exhibited as profound shock; loss of a loved one, of their lives, of freedom and independence; burden, both the physical burden and the burden of responsibility; and finally coping through faith, acceptance, empathy, and social support. CONCLUSION: Caregivers of patients discharged home with a ventricular assist device experienced significant pressures that changed over the duration of support with the ventricular assist device. Caregivers described their coping mechanisms in dealing with shock, loss, and burden. Understanding the fluctuating needs of caregivers will enable teams to provide interventions based on the situation. Future care guidelines should address the significant stresses placed on caregivers of recipients of a ventricular assist device.

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 score0.019
Threshold uncertainty score0.232

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 head0.006
GPT teacher head0.213
Teacher spread0.208 · 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

Citations36
Published2010
Admission routes1
Has abstractyes

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