Self-reported health status and lifestyle behaviours of the residents of the Town of Fort Erie, Ontario, as related to the Canadian Community Health Survey
Bibliographic record
Abstract
The purpose of this cross sectional survey design was to examine self-reported \nhealth status and lifestyle behaviours of the residents of the Town of Fort Erie, Ontario, \nas related to the Canadian Community Health Survey. Using a mail-out survey, entitled \nthe Fort Erie Survey of Health (FESH), a probability cluster sampling technique was used \nto measure self-reported health status (present health, health conditions, health \nchallenges, functional health limitations) and lifestyle behaviour (smoking, alcohol use, \ndrug use, physical activity, fruit and vegetable consumption, body weight, and gaming). \nEach variable was described and analyzed in relation to socio-economic variables, age \nand gender. The findings from this study were compared to the Canadian Community \nHealth Survey 2000/2001. Overall, 640 surveys were completed. The majority of Fort \nErie residents rated their present health as good and were satisfied with their overall \nhealth and quality of life. The main chronic conditions reported were arthritis, back pain \nand heart disease. Other main health problems reported were vision, sleeping and chronic \npain. Overall, 14.6% smoke; 58.8% engaged in physical activity either occasionally or \nnever as opposed to regularly engaging in physical activity; 52.1% did not eat the \nrequired daily fruits and vegetables; and 40.0% were in the overweight category. Persons \nwho practiced one healthy lifestyle behaviour were more likely to practice other healthy \npromoting behaviours. Therefore, health promotion programs are best designed to \naddress multiple risk factors simultaneously. The ffiSH was generally consistent with the \nCanadian Community Health Survey in the overall findings. A small number of \ninconsistencies were identified that require further exploration to determine if they are \nunique to this community.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".