Fitness, fun or friendship: A qualitative approach to understanding motivations to participate in CrossFit
Bibliographic record
Abstract
CrossFit is a community-oriented exercise program significantly growing in popularity (Wang, 2016). CrossFit consists of constantly varied functional movements performed at a high intensity, and it is generally performed in a class-structured community environment (Glassman, 2007). Given its growing popularity and community-based structure, CrossFit is an important context for understanding the impact of community on an individual's motivation to participate in a unique form of exercise. This study explored individual experiences in participating in CrossFit, along with their perceptions of the community within their gym. 217 (85 male, 132 female) current CrossFit participants (Mage = 34.49, SD = 9.83 years) responded to a series of open-ended questions relating to motivations and experiences with community at their CrossFit gym. Individual responses were thematically coded using Braun and Clarke's (2006) six-stage coding process for thematic analysis. Five distinct themes were identified to describe participants' motivations for choosing CrossFit as their form of exercise: (1) active living (health and fitness as a motivator), (2) mental health (reducing anxiety or stress), (3) personal fulfillment (intrinsic enjoyment), (4) social connections (social and community motivators) and (5) convenience and quality (services provided at the facility). In terms of the social community surrounding participation in CrossFit, three themes were identified: (1) belonging (membership and identity within the group), (2) mutual support (shared experiences offering emotional support), and (3) extending 'outside' the box (Community extending outside the gym). Overall, findings contribute to understanding how community can impact an individual's motivations to participate, adhere and enjoy exercise programs.
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 | 0.026 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".