The 2014 Survey on Living with Chronic Diseases in Canadaon Mood and Anxiety Disorders: a methodological overview
Bibliographic record
Abstract
INTRODUCTION: There is a paucity of information about the impact of mood and anxiety disorders on Canadians and the approaches used to manage them. To address this gap, the 2014 Survey on Living with Chronic Diseases in Canada-Mood and Anxiety Disorders Component (SLCDC-MA) was developed. The purpose of this paper is to describe the methodology of the 2014 SLCDC-MA and examine the sociodemographic characteristics of the final sample. METHODS: The 2014 SLCDC-MA is a cross-sectional follow-up survey that includes Canadians from the 10 provinces aged 18 years and older with mood and/or anxiety disorders diagnosed by a health professional that are expected to last, or have already lasted, six months or more. The survey was developed by the Public Health Agency of Canada (PHAC) through an iterative, consultative process with Statistics Canada and external experts. Statistics Canada performed content testing, designed the sampling frame and strategies and collected and processed the data. PHAC used descriptive analyses to describe the respondents' sociodemographic characteristics, produced nationally representative estimates using survey weights provided by Statistics Canada, and generated variance estimates using bootstrap methodology. RESULTS: The final 2014 SLCDC-MA sample consists of a total of 3361 respondents (68.9% response rate). Among Canadian adults with mood and/or anxiety disorders, close to twothirds (64%) were female, over half (56%) were married/in a common-law relationship and 60% obtained a post-secondary education. Most were young or middle-aged (85%), Canadian born (88%), of non-Aboriginal status (95%), and resided in an urban setting (82%). Household income was fairly evenly distributed between the adequacy quintiles; however, individuals were more likely to report a household income adequacy within the lowest (23%) versus highest (17%) quintile. Forty-five percent reported having a mood disorder only, 24% an anxiety disorder only and 31% both kinds of disorder. CONCLUSION: The 2014 SLCDC-MA is the only national household survey to collect information on the experiences of Canadians living with a professionally diagnosed mood and/or anxiety disorder. The information collected offers insights into areas where additional support or interventions may be needed and provides baseline information for future public health research in the area of mental illness.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".