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Record W3112538550 · doi:10.3233/wor-203339

An intervention of occupational therapy in parasports using the matching person and technology model: A case study

2020· article· en· W3112538550 on OpenAlexaboutno aff
Paloma Barbosa de Lima, Ana Cristina de Jesus Alves

Bibliographic record

VenueWork · 2020
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)PsychosocialContext (archaeology)Occupational therapyApplied psychologyMatching (statistics)AutonomyModalitiesPsychologyAssistive technologyAthletesNursingMedicinePhysical therapyComputer sciencePsychiatryHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: The practice of occupational therapy in parasports aims to improve participation in sports as an occupation, reducing barriers stemming from the environmental factors. OBJECTIVE: To analyze the process of choice, prescription and follow-up of assistive technology (AT) in competitive adolescent parasports using the theoretical model Matching Person and Technology. METHODS: Case study with 3 adolescents from 12 to 18 years old in Bocce and Para-badminton modalities was performed. Characterization Questionnaire; Quebec User Evaluation of Satisfaction with assistive Technology (B-Quest); Assistive Technology Device - Predisposition Assessment (ATD PA-Br); Brazil Criteria and Intervention Report were used. RESULTS: Level of income was intermediate and low. The AT used were handcrafted by family and coaches. The Psychosocial factors detected were low privacy, autonomy, discomfort and device appearance. Pre-intervention there was dissatisfaction with AT related to the device and the context. Post-intervention, satisfaction scores increased. CONCLUSIONS: The model was a positive guide regarding the intervention of technology in the parasport, directing the participation of the specialist with the parathletes, their family and coaches, in the continuous monitoring of its use. This was key for the satisfaction in using the AT in sports, besides contributing to their occupational performance and maintaining people with disability in parasports, increasing the possibility of adolescents becoming professional para-athletes. Further studies in this area are suggested.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.276
GPT teacher head0.516
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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