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Record W3194663660 · doi:10.1016/j.heliyon.2021.e07852

Attitudes and empathy of youth towards physically disabled persons

2021· article· en· W3194663660 on OpenAlexaboutno aff
Naveli Sharma, Virendra Pratap Yadav, Aashima Sharma

Bibliographic record

VenueHeliyon · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyNonprobability samplingPsychologyTest (biology)Descriptive statisticsPositive attitudeClinical psychologyScale (ratio)Positive correlationDevelopmental psychologySocial psychologyMedicinePopulation

Abstract

fetched live from OpenAlex

The present study aimed to investigate the attitude and empathy of youth towards physically disabled persons. This study followed a quantitative paradigm. The sample comprised of 100 participants (Male = 50; Female = 50) who were under the age range of 18-25 years. Purposive sampling was taken to gather the data. Attitudes Towards Disabled Persons (ATDP) Scale and the Toronto Empathy Questionnaire were administered on the participants. All the responses were entered on the SPSS software which was analysed through descriptive statistics, t-test, and Pearson's correlation. Findings of this study showed that both males and females had negative attitude towards physically disabled person. Furthermore, males and females were equally empathetic towards physically disabled person. Consequently, there were no gender differences in the attitude and empathy of youth towards physically disabled persons. Also, significant and positive correlation was seen between the two constructs, i.e., attitude and empathy. These results indicated a need of destigmatization about disability especially physical disability in the society.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.033
GPT teacher head0.347
Teacher spread0.314 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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