Considerations around using body mapping methods for exploring mental health experiences of para-athletes
Bibliographic record
Abstract
One-third of elite athletes experience mental illness. Individuals with disabilities are at an increased risk of mental illness. Body mapping may be a useful method to facilitate reflection in mental health research. The current study explores body mapping as a method for facilitating discussions on mental health experiences of para-athletes. Para-athletes (7 men, 3 women; impairment: 8 physical, 2 sensory, 2 intellectual) participated in virtual interviews using body mapping to reflect on the quality of their sport experiences related to mental health. Participants created an image of their body and were encouraged to make additions to the image throughout the interview. Researcher field notes and a reflective journal were used to analyze the researcher-perceived usability and adaptability of body mapping. Two themes were researcher-developed: actively supporting autonomy and individualized communication. These themes reflect the co-creation process of the body map, with (1) participants deciding if they or the researcher drew the body map (based on individual needs) and how their body would be represented (active vs. sedentary, images or words), and (2) the researcher navigating strategies for communication when the drawing was researcher-led to ensure the participant was actively co-creating the body map. Participants provided feedback throughout the interview stating body mapping aided them in reflecting on their experiences. Overall, body mapping spurred in-depth researcher-participant discussions on mental health experiences among para-athletes. Researchers interested in using body mapping are encouraged to seek participant perspectives and needs throughout the study to ensure accessibility and meaningful engagement of all individuals.
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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 | 0.150 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".