Developing content for national population health surveys: an example using a newly developed sedentary behaviour module
Bibliographic record
Abstract
BACKGROUND: While physical (in) activity surveillance has grown and continues to grow globally, surveillance of sedentary behaviour is in its infancy. As surveillance evolves to meet the changing nature of these behaviours, there is a need for the development of national health survey questions to provide accurate and consistent measures over time. The development of national health survey content is a complex, detailed and often undocumented process. The objective of this paper is to outline the process that the Public Health Agency of Canada (PHAC) and Statistics Canada took in partnership with academic experts to develop a short, flexible, sedentary behaviour module for the Canadian Health Measures Survey (CHMS) and to provide an approach for the development of future survey content. METHODS: Development of the module followed a multi-step process. The results of this paper describe this process and present a framework for content development. RESULTS: Initially, PHAC and Statistics Canada analysts worked together to identify key content required for a potential survey module. Next, this work was formalized through a contract with academic experts, the scope included a: review of existing Canadian sedentary behaviour modules; literature review linking different sedentary behaviours to health outcomes; and, international scan of modules currently in use in large national health surveys and research. The key output from both review processes was recommendations for a short sedentary behaviour questionnaire module (International Sedentary Assessment Tool). These recommendations provided an evidence-informed basis for discussions about how to revise and update the CHMS sedentary behaviour questionnaire content. Qualitative testing was undertaken and a final module was developed using survey design best practices. CONCLUSIONS: Content volume in national surveys is limited due to demand to measure core content in addition to emerging health topics while keeping surveys as short as possible. Questions must therefore, be concise, valid/reliable, evidence-based, and developed using best practices. The paper describes the development process of a new survey module addressing the emerging area of sedentary behaviour for use in a national survey that may serve as a model for future population survey content development.
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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 | 0.053 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".