A Qualitative Study of 11 World-Class Team-Sport Athletes’ Experiences Answering Subjective Questionnaires: A Key Ingredient for ‘Visible’ Health and Performance Monitoring?
Bibliographic record
Abstract
BACKGROUND: Athlete monitoring trends appear to be favouring objective over subjective measures. One reason of potentially several is that subjective monitoring affords athletes to give dishonest responses. Indeed, athletes have never been systematically researched to understand why they are honest or not. OBJECTIVE: Because we do not know what motivates professional athletes to be honest or not when responding to subjective monitoring, our objective is to explore the motives for why the athlete may or may not respond honestly. METHODS: A qualitative and phenomenological approach was used, interviewing 11 world-class team-sport athletes (five women, six men) about their experiences when asked to respond to subjective monitoring questionnaires. Interview transcripts were read in full and significant quotations/statements extracted. Meanings were formulated for each interviewees' story and assigned codes. Codes were reflected upon and labelled as categories, with similar categories grouped into an overall theme. Themes were examined, articulated, re-interpreted, re-formulated, and written as a thematic story, drawing on elements reported from different athletes creating a blended story, allowing readers a feel for what it is like to live the experience. RESULTS: Overall, four key themes emerged: (i) pursuit of the ideal-self, (ii) individual barriers to athlete engagement, (iii) social facilitators to athlete engagement; and (iv) feeling compassion from performance staff. CONCLUSIONS: Our main insight is that athletes' emotions play a major role in whether they respond honestly or not, with these emotions being driven at least in part by the performance staff asking the questions.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".