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Record W2921881156 · doi:10.5430/wje.v9n2p1

Identification of Psychological Characteristics of Flat Feet Preschool Children Using Projective Methods

2019· article· en· W2921881156 on OpenAlexvenueno aff
Sultanberk Halmatov

Bibliographic record

VenueWorld Journal of Education · 2019
Typearticle
Languageen
FieldMedicine
TopicHuman Health and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsProjective testPsychologyDevelopmental psychologyAnxietyPsychological healthPreschool educationClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Nowadays, the problem of prevention and correction of various diseases in preschool children has becomeparticularly relevant. This is due, first of all, the presence of a large number of preschoolers (84.9%) with differentdeviations in the state of health and with pathologies in the development of the musculoskeletal system in particular.The problem of studying the psychological characteristics of preschool children with orthopedic diseases seems to bequite relevant, although in contemporary scientific literature this issue has not been adequately reflected. The aim ofthe research is to identify the psychological characteristics of children of preschool age with a diagnosis of "flat feet"with the help of projective methods. The study was conducted among pupils of preschool educational institutions inMoscow, who are diagnosed with "flat feet" and conditionally healthy children (SWAD, WAD). The average age ofthe subjects was 6 years. As a result of the research, it has been found that children with a diagnosis of "flat feet",already in preschool age have certain changes in the emotional sphere, especially an increased level of anxiety andfear.

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 score0.154
Threshold uncertainty score0.217

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 integrity0.0000.000
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 head0.046
GPT teacher head0.459
Teacher spread0.414 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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