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Record W1983154197 · doi:10.12735/ier.v2i1p33

Improving Pre-Service Teachers’ Attitudes towards Individuals with Mental Illness through an Introduction to Special Education Course

2014· article· en· W1983154197 on OpenAlexvenueno aff
John W. Maag

Bibliographic record

VenueInternational Education Research · 2014
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsCourse (navigation)Mental illnessPsychologyMedical educationService (business)Mental healthMedicinePsychiatryEngineeringBusiness

Abstract

fetched live from OpenAlex

Mental illness in children and adults continues to be a controversial and misunderstood topic. Previous research has examined different populations' attitudes toward mental illness, and efforts to change community attitudes toward individuals with mental illness have included contact with the mentally ill and education programs. However, little research has examined teachers’ attitudes toward the mentally ill, nor programs for positively impacting those beliefs. The purpose of the present study was to first assess preservice teachers’ beliefs toward individuals with mental illness and to determine if the completion of an undergraduate Introduction to Special Education course could positively impact their attitudes toward individuals with mental illness. Participants included students attending three different institutions of higher education who were divided into three groups: general education majors, special education majors, and education minors. Results indicated that significant differences were obtained for all institutions and groups among their pre-test and post-test scores on the Community Attitudes Toward the Mentally Ill scale (CAMI). Implications for practice and future research are presented.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.434
Teacher spread0.389 · 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.

Study designNot applicable
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

Citations5
Published2014
Admission routes1
Has abstractyes

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