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Record W2580195243 · doi:10.30854/anf.v23.n41.2016.139

Validation of the Self-report altruism scale test in Colombian University Students

2016· article· en· W2580195243 on OpenAlexaboutno aff
David Aguilar Pardo, Jorge Martínez Cotrina

Bibliographic record

VenueÁnfora · 2016
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsnot available
Fundersnot available
KeywordsAltruism (biology)PsychologyCronbach's alphaProsocial behaviorScale (ratio)Test (biology)Social psychologyReliability (semiconductor)Clinical psychologyApplied psychologyDevelopmental psychologyPsychometrics

Abstract

fetched live from OpenAlex

Objective: to establish whether the Canadian selfreport altruism scale Questionnaire is a reliable estimate for altruistic behavior in young Colombian university students.Methodology: the self-report altruism scale test was adapted and applied. 327 university students between 18 and 25 years from five independent cohorts participated in this study. Participants should note, in 20 items, the frequency (never, once, more than once, often or very often) with which they performed altruistic behavior. The method of this study followed the protocol of the World Health Organization for these cases. There was also a correlation analysis between the score of the questionnaire and the evaluation that some close friends of each participant made in relation to the altruistic tendency of the latter.Results: cronbach's alpha, bipartition analysis and comparison of these data with those reported in other countries show that the instrument is highly reliable. The selfreport altruism scale questionnaire is a useful tool to estimate the altruistic behavior of Colombian university students.Conclusions: the relevance of developing tools to assess prosocial behavior in the country is discussed and clarified. Expanding the age range and applying the questionnaire to non-university populations, will strengthen the development of the instrument.

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 score0.024
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.010
GPT teacher head0.281
Teacher spread0.271 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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