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Record W3014193276 · doi:10.1177/1049732320912410

Falling Down the Rabbit Hole: Child and Family Experiences of Pediatric Hematopoietic Stem Cell Transplant

2020· article· en· W3014193276 on OpenAlexafffund
Christina West, Debra Dusome, Joanne Winsor, Lillian Rallison

Bibliographic record

VenueQualitative Health Research · 2020
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsBrandon UniversityUniversity of Manitoba
FundersChildren's Hospital Research Institute of Manitoba
KeywordsFalling (accident)Hematopoietic stem cellStem cellHaematopoiesisMedicinePsychologyPsychiatryBiologyGenetics

Abstract

fetched live from OpenAlex

Pediatric hematopoietic stem cell transplant (HSCT) is an intensive treatment that can be life-threatening. All family members experience distress. We conducted a grounded theory study using a family systems-expressive arts framework to develop a theoretical understanding of the family experience of HSCT. Six families (15 family members) participated in two interviews, drew an image, and were guided through a “dialoguing with images” process. Participants did not always perceive HSCT as an experience they had lived as a family and were surprised to hear other family members’ experiences. While one mother drew, she suddenly understood it was not only her ill child, but the entire family who had “fallen down the rabbit hole.” The family experience of HSCT is described across (a) the pre-HSCT trajectory, (b) family fragmentation (hospitalization), and (c) family reintegration. We identified a critical need for targeted family intervention during the transition into HSCT, throughout and following hospitalization.

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)0.005
metaresearch head score (Gemma)0.010
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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.246
GPT teacher head0.475
Teacher spread0.229 · 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

Citations26
Published2020
Admission routes2
Has abstractyes

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