Ser mãe de um filho com câncer em tratamento quimioterápico: uma análise fenomenológica
Bibliographic record
Abstract
This study had the purpose to reveal facets of the phenomenon of being a mother of a child on Drug Therapy treatment, seeking help for a better qualified assistance in pediatric oncology. It was a qualitative research, using the phenomenological method, carried out between June and August 2008. Recorded interviews were accomplished with mothers aware of the diagnosis of cancer for their children, and who were accompanying them during hospitalization for Drug Therapy treatment in a hospital specialized in oncology, in Goiânia-GO. Data analysis was based on the Method of Qualitative Analysis of Situated Phenomenon. In the discourse of mothers it was noticed an ambiguity regarding the meanings of Drug Therapy and the difficulties facing the changes, imposed to the family dynamics by the treatment of the child. The fear of the uncertainties on the course of the disease was evident, including the fear of death and recurrence. The need to take the other children away of the scenario generates significant internal conflicts and intensifies the feelings of anguish and guilt in these women as mothers. In this context, it is evident the need to redirect the approach in help the mothers who accompany the child on Drug Therapy. In these situations, it is necessary a watchful eye on children as well as their families, understanding that altogether live the process of becoming ill 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".