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Record W4405948099

Investigation of satisfaction with orthoses and its relationship to duration of use in individuals with different orthotic devices

2024· article· en· W4405948099 on OpenAlexaboutno aff
Kamil Yılmaz, Osman Karaca

Bibliographic record

VenueDergiPark (Istanbul University) · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational health in dentistry
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPhysical medicine and rehabilitationMedicine
DOInot available

Abstract

fetched live from OpenAlex

Dissatisfaction with orthosis use may lead to discontinuation and prevent treatment from being fully effective. This study aimed to investigate user satisfaction levels with orthoses used on various body parts and to determine their relationship with the duration of orthosis use. Seventy-eight orthotic users with a mean age of 12.45 ± 10.1 years participated in this observational study. Satisfaction was assessed using the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0) questionnaire, which includes subsections for device satisfaction, service satisfaction, and a total score, and the Satisfaction Evaluation Survey (SES). The QUEST results indicated a mean device satisfaction score of 4.20 ± 0.75, service satisfaction of 4.38 ± 0.77, and a total score of 4.26 ± 0.73, while the SES score averaged 13.95 ± 5.48. No significant differences were observed in satisfaction levels between lower extremity and trunk orthosis users (p>.05), or between ankle-foot orthosis and scoliosis brace users (p>.05). A weak positive correlation was found between daily orthotic use and QUEST subsections for device satisfaction (r=.364, p=.001), service satisfaction (r=.281, p=.014), and total score (r=.385, p=.001), while a weak negative correlation was noted with SES scores (r=-.306, p=.007). Overall, participants reported high satisfaction with their orthoses, regardless of the body part supported, with a weak correlation observed between daily wearing time and satisfaction levels.

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)0.000
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 score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.084
GPT teacher head0.340
Teacher spread0.257 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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