The development of the Multidimensional Perfectionism Scale for Athletes
Bibliographic record
Abstract
Perfectionism is “assumed to play a powerful and debilitating role in sport competition” (Frost & Henderson, 1991, p. 323). A recent suggestion that perfectionism is domain-specific highlights limitations of existing research that uses general measures of perfectionism with athletes (Haase, Owens, & Prapavessis, 2001). The purpose of this investigation was to develop a sport measure of perfectionism, the Multidimensional Perfectionism Scale for Athletes (MPS-A). The MPS-A was developed over 2 phases. Phase one involved defining athletic perfectionism, item development, and assessing face and content validity; phase two assessed the factor structure of the MPS-A. Canadian varsity athletes completed the survey (n = 505). Exploratory factor analysis, using maximum likelihood factor extraction and promax rotation, suggested that 36 of the original 56 items be retained, representing 7 factors and accounting for 43% of the variance. Findings are discussed in the context of present research. Further refinement of the MPS-A is suggested.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".