Implementation of the My Abilities First Tool to Empower Children and Adolescents With Disabilities: A Study Protocol
Bibliographic record
Abstract
OBJECTIVE: To present a participatory study protocol for implementing the My Abilities First (MAF) tool in Specialized Centers for Child Rehabilitation. METHOD: This is a qualitative study that will intentionally select physiotherapists, occupational therapists, children and adolescents with disabilities, aged 6-14 years, and their caregivers, in Specialized Centers for Child Rehabilitation located in the northeastern region of Brazil. The participants will answer a socio-demographic and clinical questionnaire and take part in semistructured interviews, workshops and focus groups to understand and discuss the implementation process of the MAF tool. The study will be based on Participatory Action Research (PAR) and Patient and Public Involvement (PPI). The data will be recorded and fully transcribed for Reflexive Thematic Analysis. DISCUSSION AND PERSPECTIVES: The presentation of a study protocol to implement the MAF tool has the potential to enhance changes in attitudes towards disability and individuals with disabilities. The conceptual foundation of the MAF tool, centred on positive language and attitudes, coupled with the need to identify barriers and facilitators for the implementation process, proves conducive to new insights and inclusive opportunities for children and adolescents with disabilities.
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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 | 0.081 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".