Evaluation of the electronic self-report Symptom Screening in Pediatrics Tool (SSPedi)
Bibliographic record
Abstract
OBJECTIVE: We previously developed the paper-based Symptom Screening in Pediatrics Tool (SSPedi) designed for paediatric cancer symptom screening. Objectives were to evaluate and refine the electronic mobile application (app) of SSPedi using the opinions of children with cancer. METHODS: Participants were children 8-18 years of age with cancer. Participants completed electronic SSPedi on their own and then responded to semistructured questions to determine whether they found electronic SSPedi easy or difficult to complete and understand, understood and liked the app features (audio and animation), and understood previously difficult to understand concepts with the introduction of a help menu. After each group of 10 children, responses were reviewed to determine whether modifications were required. RESULTS: 20 children evaluated electronic SSPedi. None found electronic SSPedi difficult to complete or understand. All children understood the app features and each of the 4 more difficult to understand concepts after using the help menu. 19 of 20 children thought the app was a good way to communicate with doctors and nurses. CONCLUSIONS: We finalised an electronic version of SSPedi that is easy to use and understand with features specifically designed to facilitate child self-report. Future work will evaluate the psychometric properties of electronic SSPedi.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".