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Record W2742649485 · doi:10.5539/jel.v6n4p276

The Effectiveness of Yoga on Spiritual Intelligence in Air Traffic Controllers of Tehran Flight Control Center

2017· article· en· W2742649485 on OpenAlexvenueno aff
Maryam Safara, Pejman Ghasemi

Bibliographic record

VenueJournal of Education and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsSpiritual intelligencePsychologyPopulationTest (biology)Air traffic controlApplied psychologyConsciousnessSocial psychologyClinical psychologyEngineeringEmotional intelligenceMedicine

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the efficacy of yoga on spiritual intelligence in air traffic controllers in Tehran flight control center. This was a quasi-experimental research and the study population includes all air traffic controllers in Tehran flight control center. The sample consisted of 40 people of the study population that were selected through convenience sampling method and were randomly divided into experimental group (20) and control group (n=20). This research was conducted through training yoga on the air traffic controllers to experimental group compared with the control group. The data were evaluated as the changes resulted after the intervention (post-test) compared to the previous condition (pre-test). King’s (2008) Spiritual Intelligence Self-Report Inventory (SISRI) was used to measure the variables. The data were analyzed by analysis of covariance. The results showed that yoga exercises had a significant effect on spiritual intelligence and its components (critical thinking, personal meaning production, expanded state of consciousness, transcendental consciousness) in the subjects.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.020
GPT teacher head0.372
Teacher spread0.352 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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