Part I: An Introduction to Conducting Qualitative Research in Children With Cancer
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".