Introducing a basic psychological performance demand model for sport and organisations
Bibliographic record
Abstract
This study presents the development of a basic psychological performance demand model (PDM) for sport and organisations, adopting a process view of performance underpinned by reversal theory (Apter [2001] An introduction to reversal theory. In M. J. Apter (Ed.), Motivational styles in everyday life: A guide to reversal theory (pp. 3-36). Washington, DC: American Psychological Association). Six elite coaches with extensive coaching experience at European, Commonwealth, Olympic and Paralympic Games were interviewed. Their interview statements were analysed using a combination of deductive and inductive analysis procedures for qualitative data. In conjunction with the interviewer, coaches developed PDMs for their specific sports. Analysis of interview data and coaches’ specific PDMs identified four main cross-sport themes or fundamental psychological capabilities required for meeting performance demands. These were: Mastery motivation, Decision making, Execution, and Teamship. The PDM offers a starting framework for a new basic performance model that is novel and pragmatic with potential applicability across sports and organisations. The model is useful in its existing form, but needs further testing, extended practical application and reflection by coaches, athletes, and sport psychologists. It has potential for use in other coaching contexts beyond sport, such as business, leadership development, education, and health.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".