Run with feeling: A qualitative content analysis of runner sentiment in Metro Vancouver
Bibliographic record
Abstract
• This study applies content analysis to Strava runner narratives for the first time. • Findings provide direct insight into the challenges and preferences of runners. • Results suggest the capacity of running to promote wellbeing and sense of community. This study examines features associated with runner sentiment, or feelings, based on qualitative narratives in the form of Strava social media posts among 72 men and 65 women in Metropolitan Vancouver, Canada. Posts were drawn from an Open Authorization (OAuth) portal recruiting runners who used Strava, a popular fitness-tracking social media platform. First, sentiment analysis detected the emotional polarity of posts along a spectrum between -1 to +1, indicating extreme negativity and extreme positivity, respectively. Then, a content analysis of highly positive (0.8 to 1) and negative (-1 to -0.7) posts was conducted to determine the topics associated with extreme runner sentiment. The content analysis resulted in six categories and 26 sub-categories. The categories, in order of frequency, were (1) psychological aspects, (2) interpersonal experience, (3) weather, (4) surroundings, (5) physical experience, and (6) path. Results from this study demonstrate the nuanced personal characteristics influencing runner preferences and experiences. Findings provide insight into factors that may promote or inhibit wellbeing during runs by using direct accounts from Strava runner narratives, a population primarily only studied through quantitative mapping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".