Condition Setting in Sport: A Case Study Approach to Explore Program Planning by Canadian University Coaches
Bibliographic record
Abstract
Based on a condition-setting approach derived from organizational psychology, the authors investigated the conditions that university sport coaches considered and implemented prior to a competitive season. Using a collective case study approach, semistructured interviews were conducted at two time points, with five head coaches across different sports. Student-athletes from each team (n = 5) and the high performance director from the institution were also interviewed. The data were analyzed thematically to highlight the relevant conditions for coaches and their individual athletes and were then generalized across teams within the institution. The authors’ results support the utility of the condition-setting approach outlined by Hackman for sport. Specifically, coaches emphasized the need to (a) create a team vision with clear objectives, (b) opt for athletes of best fit, (c) assign team roles and expectations, (d) confirm and allocate necessary resources, and (e) have competent and prepared team coaching. Despite the generalizability of these themes, the authors’ results highlight the need to consider the context, as both the university environment generally and each specific program were bound by unique constraints (e.g., funding). Herein, the authors discuss their findings in relation to the broader literature, propose future directions, and provide practical implications for sport coaches and institutions.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".