Exploring needs and requirements for a prototype device measuring physical activity in pediatric physical therapy: A qualitative study
Bibliographic record
Abstract
AIMS: To analyze needs and requirements of Pediatric Physical Therapists (PPTs), parents, children and adolescents with and without developmental disabilities in the future use of an activity monitor prototype (AM-p) in everyday clinical practice. METHODS: Qualitative exploratory study with a thematic analysis approach, based on Braun and Clarke's six steps. Codes derived from the analysis and central themes were collated, based on Fleuren et al.'s groupings of determinants. RESULTS: We interviewed 25 PPTs, 12 parents, and 12 children and adolescents. Within four groupings of determinants, we found nine themes: 1) development of information materials; 2) application: output visualization and ease of use; 3) design; 4) relevance and acceptance; 5) shared decision-making; 6) compatibility in daily living; 7) finances, 8) time, and 9) legislation and regulations. CONCLUSIONS: End-users have similar basic needs, with individual fine-tuning to be addressed during further development of the AM-p. A child-friendly design, information material, and an easy-to-use application to read and interpret results, need to be developed. Efficient training for PPTs is important for the use of the AM-p and analysis of results. Communication between PPTs and children as well as parents enhances shared decision-making. We recommend involving diverse end-users to enable maximum customization of the AM-p.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".