Psychometrische Eigenschaften einer deutschsprachigen Adaptation des Test of Performance Strategies (TOPS)
Bibliographic record
Abstract
Zusammenfassung. Der Test of Performance Strategies (TOPS; Thomas, Murphy & Hardy, 1999 ) ist ein Fragebogen zur Erfassung psychischer Techniken und Fertigkeiten, die Athleten im Training und Wettkampf verwenden. Auf der Basis einer Stichprobe von 378 Sportlern verfolgt dieser Beitrag das Ziel, die psychometrische Qualität einer deutschsprachigen Adaptation des TOPS (TOPS-D) zu untersuchen. Konfirmatorische Faktorenanalysen ergeben für den Trainings- sowie den Wettkampfkontext Hinweise für die Angemessenheit der postulierten Acht-Faktoren-Modelle. Die inneren Konsistenzen der Skalen schwanken zwischen .56 und .86 und betragen im Mittel .76. Der TOPS-D diskriminiert zwischen Athleten unterschiedlicher Leistungsstärke und korreliert im erwarteten Sinn mit einer deutschsprachigen Version des Ottawa Mental Skills Assessment Tool (OMSAT-3*; Durand-Bush, Salmela & Green-Demers, 2001 ). Insgesamt bestätigen die Ergebnisse zum TOPS-D weitgehend jene zur Originalversion. Wenngleich einzelne Skalen revisionsbedürftig sind, lässt sich der TOPS-D in der Forschung und – mit Vorbehalt – in der Beratungspraxis im deutschen Sprachraum einsetzen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".