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Profile of Pediatric Occupational Therapy Practice in Kuwait: A Qualitative Study

2022· article· en· W4289175112 on OpenAlexaboutno aff
Asmaa Alenezi, Sara Alsairafi

Bibliographic record

VenueScholars Journal of Applied Medical Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyMedicineChristian ministryTest (biology)SpecialtyFamily medicinePhysical therapy

Abstract

fetched live from OpenAlex

Pediatrics specialty is the most occupational therapy practice in Kuwait. Therefore, the purpose of this survey is to establish a profile of pediatric occupational therapy practice in Kuwait. Moreover, it aims to recognize the most common diagnoses that are referred to the pediatrics occupational therapy in Kuwait as well as identify the most common assessments, frame of reference, and intervention techniques that are used in the area of practice. In addition, there are also many studies conducted about pediatric occupational therapy profiles in Australia, Canada, and United States, However, there are no studies about pediatric occupational therapy profiles in Kuwait (Australian Occupational Therapy Journal, 2005). Therefore, the importance of this research is to follow a successful role model similar to Canada, Australia, and the United States. The Methodology was is ex post facto survey that is adapted to conduct the data. The sample is all pediatric occupational therapists working in both Ministry of Health and the privet educational sector in Kuwait receive the survey. The sample size was 260 therapists but the responding participants were 217 therapists. The result showed that the most commonly used assessments were the Developmental Test of Visual Motor Integration, Peabody Developmental Motor Scales, Bruininks-Oseretsky Test of Motor Proficiency, and the Sensory Profile.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.427
Teacher spread0.357 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

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