Misery loves company: Exploring mixed martial artists' experiences of pain with teammates and coaches
Bibliographic record
Abstract
Athletes often experience pain during their sport performances and in their everyday lives (Spencer, 2012). However, there is little research that has explored how athletes form understandings of pain, and how social interactions contribute to athletes' understandings of pain. The purpose of this study was to explore mixed martial artists' experiences of pain while training for a MMA competition. Specifically, this research examined the meanings that fighters attribute to their pain experiences and how teammate and coach relationships contributed to fighters' experiences of pain. A multiple case study approach (Stake, 1995) was used to study pain among four amateur and professional mixed martial artists and their training partners (total N = 7). Data were collected over four months from the beginning of a training camp to the completion of a fight, using semi-structured interviews, participant observation, athlete video diaries, and video recordings of training sessions and fights. Data were analyzed using interpretative phenomenological analysis (Smith, Flowers, & Larkin, 2009). Results pertained to: (a) conceptualization and distinctions between physical pain, injury, and emotional pain; (b) the process of giving and receiving pain for fighters (e.g., pain built connections/trust between teammates); and (c) how teammates enabled fighters to move through their pain experiences. These findings are discussed with respect to fighters' relationships with pain (e.g., bringing self-awareness and attention to vulnerabilities) and how pain 'callused' or toughened fighters physically and emotionally. The researcher will also discuss implications for pain research in sport, and the use of video diaries in qualitative research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".