How do runners select their shoes? An in-store experience
Bibliographic record
Abstract
We aimed to identify factors that influence running shoe selection and how salespeople and runners experience the in-store selection process. In a cross-sectional design, we surveyed 101 runners (buyers and non-buyers) and 38 salespeople in specialty running stores. Surveys contained questions about demographics, factors influencing shoe choice, sources of footwear advice/education, conscious behaviour, and perceived influence of salespeople on selection. There were no significant differences between buyers and non-buyers regarding how much runners thought about their purchases (i.e., level of consciousness). Salespeople were significantly younger than runners and believed a greater number of factors and sources of advice influenced shoe selection. Runners most frequently identified fit, comfort, and gait analysis or injury prevention as most influential in selecting shoes, in that order. Salespeople believed comfort was the most important for runners. Buyers and non-buyers prioritised advice on running shoes from salespeople, friends, and family, while salespeople primarily got their information from peers. Buyers and non-buyers visiting speciality running stores largely reflect the same population. Salespeople advising runners significantly differed from their target clientele in several regards and overestimated their influence on runners’ selection. We caution runners to carefully consider the advice from salespeople as many employees make recommendations that are not evidence-based and may have limited experience.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".