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Record W4253891048 · doi:10.1177/104345420001700402

Part I: An Introduction to Conducting Qualitative Research in Children With Cancer

2000· review· en· W4253891048 on OpenAlexaff
Roberta L. Woodgate

Bibliographic record

VenueJournal of Pediatric Oncology Nursing · 2000
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsQualitative researchGrounded theoryNarrativeChildhood cancerPsychologyPediatric cancerMedicineCancerSociology

Abstract

fetched live from OpenAlex

Over the past decade, pediatric nurse researchers have acknowledged the need to study children's cancer illness experiences within the qualitative research framework. Support for more qualitative research is based on the belief that it will afford researchers the opportunity to get closer to understanding children's perspectives of their cancer experience. A priori theories or generalizations by the researcher are not imposed; therefore, information emerging from the research is believed to be more a reflection of the perspectives of child participants and not adult researchers. Although pediatric oncology nurses may be interested in using more qualitative methods in their research, deciding on the appropriate qualitative research design may not always be so evident, considering that the adoption of qualitative inquiry in the study of childhood cancer is in its infancy. Accordingly, the purpose of this article is to increase the reader's understanding of the use of the qualitative research paradigm in the study of children's experiences with cancer. An overview of four qualitative research designs that pediatric oncology nurse researchers may adopt is presented. Specifically, the qualitative designs of grounded theory, ethnography, phenomenology, and biography or illness narratives are examined. To facilitate discussion, each of the four designs are applied to the study of symptom experiences in children with cancer.

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.305
GPT teacher head0.580
Teacher spread0.276 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations20
Published2000
Admission routes1
Has abstractyes

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