Exploring the Occupational Physical Activity Levels in young Adult Restaurant Servers.
Bibliographic record
Abstract
Physical activity (PA) decreases in late adolescence and young adulthood when this age cohort enters the workforce with many being employed in the foodservice industry. Daily energy expenditure can be divided between occupational physical activity (OPA) and leisure-time physical activity (LTPA). Although LTPA is known to be associated with positive health benefits, a limited number of studies have investigated the influence of OPA on LTPA in young restaurant servers. This study measured via accelerometry, OPA and LTPA for a 7-day period from a sample of young adult servers (n = 7, 23-29 years old) from two restaurants. Resting metabolic rate was measured via indirect calorimetry and inputted into the AMP 331 accelerometer for energy expenditure calculations. Energy expenditure patterns were compared to age- and sex-specific normative data, occupational classifications, and the Canadian PA Guidelines to Healthy Active Living (CPAG). Energy expenditure results and step count values were higher for working versus non-working days and working versus non-working periods. Daily, working energy expenditure was approximately equal to normative data, while non-working daily energy expenditure was lower. The working period energy expenditure placed this population in the ‘exceptionally active’ OPA classification. Minimum PA levels, based on CPAG were met each day, however 10,000-steps-per-day were only achieved on working days. None of the participants logged LTPA and therefore 100% of their PA came from OPA. More research is needed over a longer duration and at different times in the year to identify the effect OPA has on LTPA in this population.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".