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Record W2123504653 · doi:10.1177/1043454211418665

Parental Perspectives on Inpatient Versus Outpatient Management of Pediatric Febrile Neutropenia

2011· article· en· W2123504653 on OpenAlexafffund
Caroline Diorio, Julia Martino, Katherine Boydell, Marie‐Chantal Ethier, Chris Mayo, Richard E. Wing, Oliver Teuffel, Lillian Sung, Deborah Tomlinson

Bibliographic record

VenueJournal of Pediatric Oncology Nursing · 2011
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsThematic analysisFebrile neutropeniaMedicinePreferenceInpatient careFamily medicineHealth careNeutropeniaNursingIntensive care medicineQualitative researchInternal medicine

Abstract

fetched live from OpenAlex

To describe parent preference for treatment of febrile neutropenia and the key drivers of parental decision making, structured face-to-face interviews were used to elicit parent preferences for inpatient versus outpatient management of pediatric febrile neutropenia. Parents were presented with 4 different scenarios and asked to indicate which treatment option they preferred and to describe reasons for this preference during the face-to-face interview. Comments were recorded in writing by research assistants. A consensus approach to thematic analysis was used to identify themes from the written comments of the research assistants. A total of 155 parents participated in the study. Of these, 80 (51.6%) parents identified hospital-based intravenous treatment as the most preferred treatment scenario for febrile neutropenia. The major themes identified included convenience/disruptiveness, physical health, emotional well-being, and modifiers of parental decision making. Most parents preferred hospital-based treatment for febrile neutropenia. An understanding of issues that influence parental decision making may assist health care workers in planning program implementation and further support families in their decision-making process.

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 score0.167
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.056
GPT teacher head0.346
Teacher spread0.289 · 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

Citations16
Published2011
Admission routes2
Has abstractyes

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