Family expectations and demand for home-based videogaming therapy in children with cerebral palsy in Costa Rica: a mixed methods study
Bibliographic record
Abstract
PURPOSE: To understand the expectations and demand for a movement-tracking videogame (Bootle Blast) for home-based, upper limb (UL) rehabilitation among Costa Rican children with cerebral palsy (CP). METHODS: success criteria (i.e., recruitment rate, having an appropriate screen and space to play, setting a weekly play time goal (PTG) ≥45 min, identifying one UL therapy goal). The DEPICT model for collaborative qualitative analysis was used in the thematic analysis of interview data. RESULTS: Fifteen dyads participated (1.6 ± 1 recruited/month). All had a flat-screen TV in a suitable location to play, were able to set a UL therapy goal, and established PTGs ranging from 45-120 min per week. Identified themes were: 1) Socio-cultural factors heighten demand, 2) Feelings of hope prevail for the intervention, and 3) Collaborative goal setting supports realistic expectations for Bootle Blast. CONCLUSIONS: Dyads had positive and realistic expectations about implementing the proposed videogaming intervention. This study provides insights on tailoring a family-centered, therapy gaming intervention to improve access to motor rehabilitation for children with CP in rural/remote settings and low-middle income countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".