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Record W2804569759 · doi:10.1123/jsep.2017-0193

The Development and Psychometric Properties of the Multidimensional Assessment of Teamwork in Sport

2018· article· en· W2804569759 on OpenAlexaff
Desmond McEwan, Bruno D. Zumbo, Mark Eys, Mark R. Beauchamp

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsWilfrid Laurier UniversityUniversity of British Columbia
Fundersnot available
KeywordsPsychologyTeamworkAthletesConstruct validityConfirmatory factor analysisConstruct (python library)Reliability (semiconductor)Applied psychologySport psychologyStructural equation modelingTeam sportValiditySample (material)PsychometricsSocial psychologyClinical psychologyStatisticsPhysical therapyComputer scienceMathematics

Abstract

fetched live from OpenAlex

The purpose of this research was to develop a questionnaire to assess the multidimensional construct of teamwork in sport and to examine various aspects of validity related to that instrument. A preliminary questionnaire was first created, and feedback on this instrument was then obtained from a sample of team-sport athletes (n = 30) and experts in sport psychology (n = 8). A modified version of the questionnaire was then completed by 607 athletes from 48 teams, and 5 multilevel confirmatory factor analyses were conducted to examine the structural properties of data derived from this instrument. Evidence of adequate model-data fit along with measurement reliability was obtained for each of the 5 models. Taken together, the results from this research provide support for the content, substantive, and structural aspects of construct validity for data derived from the 66-item Multidimensional Assessment of Teamwork in Sport.

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)0.016
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.030
GPT teacher head0.336
Teacher spread0.306 · 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 designBench or experimental
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

Citations31
Published2018
Admission routes1
Has abstractyes

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