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Record W2342665467 · doi:10.5539/gjhs.v8n12p141

Knowledge and Attitude of Primary School Teachers in Tehran/Iran towards ADHD and SLD

2016· article· en· W2342665467 on OpenAlexvenueno aff
Mojgan Khademi, Sepideh Rajeziesfahani, Simasadat Noorbakhsh, Leili Panaghi, Rozita Davari-Ashtiani, Katayoon Razjouyan, Nina Salamatbakhsh

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsChristian ministryPsychologySchool teachersAttention deficitAttention deficit hyperactivity disorderPositive attitudeDevelopmental psychologyMedical educationClinical psychologyMedicineMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

<p>The purpose of this study was to assess the knowledge and attitude of primary school teachers in Tehran (Iran) towards attention deficit hyperactivity disorder (ADHD) and specific learning disability (SLD). This study was conducted on 205 primary school teachers in Tehran. Using multi-stage sampling, 25 schools were selected randomly. The selected teachers completed a self-report questionnaire on knowledge and attitude towards ADHD and SLD. They were found to have average knowledge of as well as mostly neutral attitudes towards SLD and ADHD. There was a positive significant relationship between knowledge and attitude scores of the participants on the two disorders. Regarding students with ADHD or SLD, instead of referring to specialists, most teachers chose to inform the parents. Our findings mainly indicate average knowledge and attitude scores for both ADHD and SLD-compared to lower findings in previous studies. Those responsible for organizing and holding in-service training workshops on these issues need to have complete mastery and up-to-date information. It is necessary that the results of such studies be used in educational planning and policy making in the Ministry of Education. </p>

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)0.002
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 score0.131
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.066
GPT teacher head0.401
Teacher spread0.335 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

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