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Record W2798152320 · doi:10.1177/0193945918770440

Child and Parent Access to Transplant Information and Involvement in Treatment Decision Making

2018· article· en· W2798152320 on OpenAlexaff
Kristin Stegenga, Rebecca D. Pentz, Melissa A. Alderfer, Wendy Pelletier, Diane L. Fairclough, Pamela S. Hinds

Bibliographic record

VenueWestern Journal of Nursing Research · 2018
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsAlberta Children's Hospital
FundersNational Institutes of HealthOncology Nursing Foundation
KeywordsSiblingMedicinePerceptionFamily medicinePediatricsPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Pediatric stem cell transplant processes require information sharing among the patient, family, and clinicians regarding the child's condition, prognosis, and transplant procedures. To learn about perceived access to transplant information and involvement in decision making among child family members (9-22 years old), we completed a secondary analysis of 119 interviews conducted with pediatric patients, sibling donors, nondonor siblings/cousins, and guardians from 27 families prior to transplant. Perceptions of information access and involvement in transplant-related decisions were extracted and summarized. We compared child member perceptions to their guardians' and examined differences by child age and gender. Most child members perceived exclusion from transplant (79%) and donor (63%) information and decisions (63%) although this varied by child role. Gender was unrelated to involvement; older age was associated with less perceived exclusion. Congruence in perspectives across children and guardians was evident for eight (30%) families, most of whom ( n = 7) excluded the children.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.171
GPT teacher head0.490
Teacher spread0.320 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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