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Record W4249439945 · doi:10.12689/jmep.2020.901

Personality and participation in aerobics, circuit training, and Tai Chi

2020· article· en· W4249439945 on OpenAlexaff
Judy Frith, George Wilson, John Kerr

Bibliographic record

VenueJournal of Motivation Emotion and Personality Reversal Theory Studies · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Training Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCircuit trainingTraining (meteorology)PersonalityPsychologyApplied psychologySocial psychologyMultimediaMedical educationComputer sciencePhysical medicine and rehabilitationMedicineGeography

Abstract

fetched live from OpenAlex

The purpose of the present study was to compare the personality characteristics of 93 participants engaged in aerobics, circuit training, or Tai Chi for a period of six months or more using the Motivational Style Profile (MSP: Stepwise discriminant function analysis with Wilks' Lambda statistical criterion was employed in separate statistical analyses with the ten MSP subscales as predictors of membership of three exercise groups. Motivational Style Profile dominance scores produced non-significant discriminant functions. However, the MSP subscales produced significant discrimination between the three exercise groups. The most accurate prediction of correct group membership was 66.7% for aerobics, with 64.7% accuracy for circuit training, and 51.7% of correct classifications for Tai Chi. Discriminant function 1 separated the Tai Chi group from the other two exercise groups and discriminant function 2 differentiated the circuit training group from both Tai Chi and aerobics groups. The Tai Chi group had high levels of alloic-mastery and low levels of both autic-mastery and arousal-seeking. The circuit training group had high levels of negativism and arousal-seeking. The aerobics group had low alloic-mastery, and high autic-mastery in comparison to the Tai Chi group and lower levels of negativism and arousal-seeking than the circuit training group. The findings are important because they identified differences between the three activity groups rather than between exercise activity and non-activity groups. Also, MSP subscale scores were more sensitive discriminators of exercise group differences than their derived dominance values.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.501
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 head0.314
GPT teacher head0.459
Teacher spread0.145 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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