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Record W4390514823 · doi:10.52881/gsbdergi.1236185

INVESTIGATION OF CAREGIVER BURDEN AND OCCUPATIONAL THERAPY INTERVENTION IN DISABLED INDIVIDUALS IN COVID 19 TERM

2024· article· en· W4390514823 on OpenAlexaboutno aff
Ayşe Göktaş, Demet Biçki

Bibliographic record

VenueGazi Sağlık Bilimleri Dergisi · 2024
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyMedicineIntervention (counseling)Coronavirus disease 2019 (COVID-19)Physical therapyPsychologyNursingInternal medicine

Abstract

fetched live from OpenAlex

Objective It was conducted to determine the factors affecting the occupational therapy training and the care burden of parents in individuals who were to learn in the Covid 19. Methods: The study sample consisted of individuals whose average age was 23.27 ±311.77 years in a private education institution in Ankara. Caregiver burden of parents was evaluated with Zarit Caregiver Scale (ZCS). Activity performance and satisfaction levels of the individuals were evaluated with the Canadian Activity Performance Measurement (COPM). The intervention was applied for 12 weeks. Results It was determined that 44.4 % of the caregivers had a moderate care burden. A statistically significant difference was obtained when comparing the ZCB before and after COVID-19 (p=0.006). A statistically significant decrease was found in the number of activities during the COVID 19 period (p=0.000, t=4.89). Conclusions: Occupational therapy intervention resulted in improvement in the activity performance of individuals. It is necessary to plan more studies that include evaluation and intervention approaches in the fields of activity performance and satisfaction in the field of occupational therapy.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.410
Teacher spread0.304 · 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 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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