MétaCan
Menu
← Back to cohort
Record W3118296187 · doi:10.5539/mas.v15n1p63

Emotional Organization Strategies for Educational Counselors in Government Schools and its Relationship to Some Variables

2020· article· en· W3118296187 on OpenAlexvenueno aff
Areen Mohammed Alghzewat Alkhawaldeh

Bibliographic record

VenueModern Applied Science · 2020
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsSample (material)Scale (ratio)PsychologyGovernment (linguistics)Face (sociological concept)Emotional intelligenceApplied psychologySocial psychologyMedical educationMedicineSociology

Abstract

fetched live from OpenAlex

This study aimed to identify the level of emotional organization strategies among educational counselors in government schools according to the variable (gender and years of experience), and the study sample was chosen randomly, as the sample reached (93) female and male counselors, of whom (41) were male and (52) female counselors, the researcher used the scale of the emotional organization strategies, which were developed for the purposes of this study, where the results showed that the level of the emotional organization strategies was "average" among the study sample individuals on the scale as a whole and the sub-dimensions, as the levels of emotional organization as a whole are higher for males as well as for those with more than ten years of experience and in the light of the results revealed by the study, the researcher recommends the need to take care of the counselors by exposing them to training guidance programs to develop emotional organization strategies that increase their professional and self-efficacy to face professional and life pressures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.279
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied Science→Same topicChild and Adolescent Psychosocial and Emotional Development→French-language works237,207→