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Record W4295761760 · doi:10.1111/petr.14388

The experiences of children with a cardiac transplant, their families and health care providers in the <scp>COVID</scp>‐19 pandemic

2022· article· en· W4295761760 on OpenAlexafffundabout
Rosslynn Zulla, David Nicholas, Lori J. West, Sarah Chan, Marie McCoy, Simon Urschel

Bibliographic record

VenuePediatric Transplantation · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsStollery Children's HospitalNorthern Alberta Institute of TechnologyUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPandemicMedicineHealth careFamily centered careFamily medicineQualitative researchCoronavirus disease 2019 (COVID-19)NursingDisease

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic has had deleterious impacts on pediatric patients and families, as well as the healthcare providers who have attended to their care needs. METHODS: In this qualitative study, children with a cardiac transplant, as well as their families and healthcare providers were interviewed to explore the impact of the COVID-19 pandemic on pediatric care, as well as on patients' and their families' daily lives. Participants were recruited from a children's hospital in western Canada. Fifteen caregiving parents of transplanted children, 2 young patients, and 8 healthcare providers participated in interviews. RESULTS: Findings highlighted how families and their healthcare providers experienced pandemic-related shifts. Themes highlighted experiences, which entailed (1) initially hearing about the COVID-19 pandemic; (2) learning about their new reality; (3) adjusting to the pandemic; (4) adjusting to shifts in pediatric services; (5) evolving a view on the future, and (6) offering recommendations for cardiac care in a pandemic. CONCLUSIONS: Study implications emphasize the need to critically reflect on, and advance, methods of helping young patients and their families in pandemic circumstances, and supporting healthcare providers.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.930

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 studies0.0010.000
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 head0.029
GPT teacher head0.328
Teacher spread0.299 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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