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Record W3026277041 · doi:10.5539/ies.v13n6p14

Examining the Relationship Between School Mindfulness and Organizational Trust

2020· article· en· W3026277041 on OpenAlexvenueno aff
Erkan Tabancalı, Gülay Öngel

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsnot available
Fundersnot available
KeywordsMindfulnessPsychologyPrincipal (computer security)Regression analysisScale (ratio)Data collectionApplied psychologySample (material)Social psychologyClinical psychologySociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

The purpose of the study is to investigate the relationship between organizational trust and school mindfulness. To achieve this purpose, a quantitative approach and relational design were preferred, and School Mindfulness Scale and Organizational Trust Scale were used as data collection tools. The study sample consisted of 495 participants working in public schools in İstanbul province. The data was evaluated using arithmetic mean, correlation and regression analysis. According to the results, trust in the principal and colleagues improves school mindfulness. Furthermore, trust in the principal increases the mindfulness of the school principal, while trust in colleagues enhances mindfulness among the faculty. It can therefore be said that school principals may prefer to strengthen organizational trust to increase the level of mindfulness within the school.

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)0.002
metaresearch head score (Gemma)0.015
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.255
GPT teacher head0.467
Teacher spread0.212 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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