Health Club Attendance, Expectations and Self-control
Bibliographic record
Abstract
Using a unique dataset on health club attendance from Quebec, we look at the relationship between actual and expected attendance and how these relate to a reported measure of self-control problems at the time of contract signing. We find that a large majority of contract choices appear inconsistent purely on financial grounds: 47.5% of members would be better off paying the fee for a single visit each time they go to the gym rather than signing a long-term contract. The median total cost of making a mistake on this decision is $262. We then compute that almost all members have made the right decision once we use subjective expectations of the number of visits per week at the time of contract choice. Next, we study how actual attendance following contract choice is related to baseline reports of self-control. We find that reports of self-control problems at baseline are associated with low future attendance and that attendance decreases faster, in particular after New Year. Finally, those with a large gap between expected and realized attendance have a much lower probability of contract renewal. Our results are consistent with a model of health club participation where agents underestimate the severity of their self-control problems.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".