When good coaches exert harmful behaviours: Understanding the use of emotionally abusive coaching practices
Bibliographic record
Abstract
The purpose of this study was to explore coaches' reflections on emotional abuse in the coach-athlete relationship. A constructivist and symbolic interactionist approach to grounded theory was employed. Participants included nine elite coaches, seven male and two female. Coaches ranged in age from 38-68 years of age (M=52 ± 11.12), with 18-47 years of coaching experience (M=28.22 ± 9.92). In-depth semi-structured interviews were conducted with each participant, and data were coded using open, axial, and selective coding techniques. The following themes emerged from the raw data; coaches' care for the athletes, the nature of the coach-athlete relationship, previous use of harmful coaching behaviours, past normalization/justification of harmful coaching practices, and perceived reasons for change in coaching behaviour. Coaches' reflections about the reasons for choosing to use emotionally abusive behaviours in the coach-athlete relationship are interpreted to suggest two distinct origins of emotional abuse. Additionally, themes of data on the coaches' perceived reasons for change in coaching behaviour are categorized within the framework of social learning theory. Applied and theoretical recommendations are discussed. Acknowledgments: Social Sciences and Humanities Research Council of Canada and Sport Canada
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".