Extreme Sport/Adventure Activity Correlates in Gynecologic Cancer Survivors
Bibliographic record
Abstract
OBJECTIVE: We examined the demographic, medical and behavioral correlates of participation and interest in extreme sport/adventure activities (ESAA) in gynecologic cancer survivors. METHODS: A random sample of 621 gynecologic cancer survivors in Alberta, Canada, completed a mailed self-report questionnaire assessing medical, demographic, and behavioral variables and participation and interest in ESAA. RESULTS: Multivariate analyses revealed that gynecologic cancer survivors were more likely to participate in ESAA if they met aerobic exercise guidelines (OR=1.75 [95%CI:1.02-2.99]), had better general health (OR=1.71 [95%CI: 1.01-2.90]), had cervical or ovarian cancer (OR=1.95 [95%CI:0.97-3.93]), were employed (OR=1.71 [95%CI:0.95-3.08]), and were of healthy weight (OR=1.58 [95%CI:0.93-2.68]). Moreover, gynecologic cancer survivors were more likely to be interested in trying an ESAA if they had cervical or ovarian cancer (OR=1.76 [95%CI:0.94-3.27]) and were meeting the strength exercise guidelines (OR=1.68 [95%CI:0.95-2.98]). CONCLUSIONS: Medical, demographic, and behavioral variables correlate with participation and interest in ESAA in gynecologic cancer survivors. The pattern of correlates suggests that gynecologic cancer survivors are more likely to participate in ESSA if they have the physical capability and financial resources. Interventions to promote ESAA in gynecologic cancer survivors need to address these 2 key barriers.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".