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Record W4390699616 · doi:10.31234/osf.io/t3swh

The Impact of Perceived Racism on the Mental Health and Academic Motivation of International Students in Kerala

2024· preprint· en· W4390699616 on OpenAlexaff
ALIDA DORIN, Anuradha Baburaj, Paballo K. Lerotholi, Noora Ansar, A Lekshmiparvathy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutions123 Certification (Canada)
Fundersnot available
KeywordsPsychosocialRacismNonprobability samplingPsychologyMental healthSample (material)Inclusion (mineral)Social psychologyPopulationClinical psychologyMedicineSociologyGender studiesPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

The study explores the psychosocial issues of international students studying in Kerala, and the impact of perceived racism on their mental well-being and academic motivation. Male and female international students (N=32) studying various disciplines under the universities in Kerala were selected for the study through convenience sampling. 10 participants were selected through purposive sampling from the sample population of 32 for a telephonic interview. Content analysis was used for discerning their responses. They reportedlack of inclusion in the classroom, concerns regarding language barriers, academic demotivation, discrimination, and dissatisfaction. Questionnaires were distributed to all the 32participants to measure their perceived racism, academic motivation and mental health.Correlation and t-test were used for statistical analysis. Perceived racism was found to have significant relationships with multiple aspects of mental health and academic motivation, indicating a negative impact on the latter aspects. Being one of the earliest studies concerning the well-being of international students in Kerala, this study highlights the need for further research in the area to better comprehend the problems and resolve them.

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 score0.138
Threshold uncertainty score0.454

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 integrity0.0000.001
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 head0.083
GPT teacher head0.471
Teacher spread0.388 · 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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