P1-119 Low social support as a risk factor for a major depressive episode in Canadian community-dwelling seniors
Bibliographic record
Abstract
Background Major depression represents a great cause of disease burden worldwide. Further, the proportion of Canadian citizens aged 65 years of age and older is rapidly growing. Despite this, there is a lack of longitudinal data on risk factors for a major depressive episode in seniors. While current literature has established social support as an important factor in the development and prevention of a major depressive episode, comprehensive measures of social support are rarely employed. A longitudinal approach to examining the relationship between depression and comprehensive social support tools has yet to be conducted in Canada. Methods This study will use 12 year population-based longitudinal data from the NationalPopulation Health Survey, collected by Statistics Canada The survey will be restricted to individuals aged 65 years of age and older. Demographic and socioeconomic characteristics of the sample will be presented. The 2-year and 10-year incidence proportions of major depression in seniors will be estimated. The cross-sectional and longitudinal association between social support and a major depressive episode will be examined using multivariate logistic regression. Results This study will meet the thesis requirements for a Master's of Epidemiology. At the time of abstract writing, no results are available for abstract inclusion.Results and conclusions will be available and will be presented at the World Congress of Epidemiology conference in August 2011.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".