Recreational fishing, health and well-being: findings from a cross-sectional survey
Bibliographic record
Abstract
We evaluated the associations between marine recreational fishing, stress, seafood consumption, and sleep quality in a cross-sectional questionnaire-based survey of a convenience sample of 244 fishers recruited in 2019 in Spain. Fishers' stress levels were moderate, with a mean stress index score of 36.4 units on a scale from 14 (very low stress) to 70 (very high). Their average emotional condition was positive, with a mean index of negative affect of 7.8 units on a scale from 5 (very low negative affect) to 25 (very high). Seafood intake was low, with a mean index of seafood in diets of 38.0 units on a scale from 20 (very low seafood consumption) to 160 (very high). Fishers' perceived quality of night sleep was good because the mean index of sleep problems was 39.5 units on a scale from 21 (very low sleep problems) to 107 (very high). Each hour of self-reported monthly fishing activity was associated with 0.016 units of lower stress score. Thus, the most engaged fishers reported up to 15.4% lower stress score than less avid fishers. Since recreational fishing is a highly accessible outdoor activity for people in older age groups, it is possible that public health could be improved by access to sustainably managed recreational fisheries. Fishing engagement was positively associated with seafood intake. Each hour of fishing per month was associated with one-unit higher seafood consumption. The higher seafood consumption observed among avid recreational fishers compared with less avid fishers might have health implications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".