MétaCan
Menu
Back to cohort
Record W4380880377 · doi:10.1177/10497323231182892

The Sensory Experience of Waiting for Parents of Children Awaiting Transplant: A Narrative Ethnography

2023· article· en· W4380880377 on OpenAlexaff
Kristina Smith, Kimberley Widger, Kelly P. Arbour‐Nicitopoulos, Barbara E. Gibson

Bibliographic record

VenueQualitative Health Research · 2023
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of TorontoVancouver Coastal Health
Fundersnot available
KeywordsNarrativeEthnographyPsychologyBone marrow transplantAestheticsTransplantationMedicineSociologyBone marrow transplantationArtLiterature

Abstract

fetched live from OpenAlex

Despite the senses being a valuable source of knowledge, little research has explored the sensory process of medical experiences. This narrative ethnographic study investigated how the senses shaped parents' experiences of waiting for their child to receive a solid organ, stem cell, or bone marrow transplant. Six parents from four different families primarily participated in sensory interviews as well as observations that explored the question: How do parents experience waiting using the five senses? Our narrative analysis suggested that parents' bodies stored sense memories, and they re-experienced stories of waiting through the senses and 'felt realities'. In addition, the senses transported families back to the emotional experience of waiting, which highlighted the longevity of waiting after receiving a transplant. We discuss how the senses provide important information about the body, waiting experiences, and the environmental contexts that mediate waiting. Findings contribute to theoretical and methodological work exploring how bodies are implicated in producing narratives.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.008
Scholarly communication0.0030.005
Open science0.0010.005
Research integrity0.0010.002
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 head0.451
GPT teacher head0.608
Teacher spread0.158 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueQualitative Health ResearchSame topicEmpathy and Medical EducationFrench-language works237,207