Construction and validation of Comics at children with acute lymphocytic leukemia
Bibliographic record
Abstract
Abstract Introduction Cancer has an impact on the lives of children and their families. Comics can be a strategy to strengthen the bond and communication between professional/patient/family. Objective To develop and validate an instructional/educational material, in the format of Comics, aimed at children hospitalized with acute lymphocytic leukemia. Methodology Methodological study developed in nine stages: preparation of the research project; content definition and selection; language adaptation; inclusion of illustrations; construction of a pilot material; validation of the material; layout; final printing and availability. Validation took place with 10 specialists between March and May 2022, using the Health Education Content Validation Instrument. Results 5 Comics were created, with 6 main characters, requiring 63 hours of work. They were divided by themes (gastrointestinal disorders; hemorrhagic cystitis; problems related to self-esteem and self-image; risk of infection and bone pain) that obtained a satisfactory global Content Validity Index between 0.78 and 0.87. Conclusions and implications for practice Comics can be used as an attractive and reliable source of information about the disease, supporting information during hospitalization and preparation for discharge.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".