Developing a coordinated Canadian post-secondary surveillance system: a Delphi survey to identify measurement priorities for the Canadian Campus Wellbeing Survey (CCWS)
Bibliographic record
Abstract
BACKGROUND: Interventions that promote health and wellbeing among young adults are needed. Such interventions, however, require measurement tools that support intervention planning, monitoring and evaluation. The primary purpose of this study is to describe the process in developing a framework for a Canadian post-secondary health surveillance tool known as the Canadian Campus Wellbeing Survey (CCWS). METHODS: Nineteen health service providers or mental health experts from 5 Canadian provinces participated in a 3-round Delphi survey by email and an in-person roundtable meeting to identify wellbeing and health behavior measurement priorities and indicators for the CCWS. RESULTS: The final CCWS framework consisted of 9 core sections: mental health assets, student experience, mental health deficits, health service utilization/help seeking, physical health/health behaviors, academic achievement, substance use, nutrition, and sexual health behavior. Panelists generally agreed on a set of indicators, and reached consensus for at least one indicator per core section. CONCLUSION: This CCWS framework is the first step in developing a common surveillance mechanism tailored to the Canadian postsecondary context. Future work will include online consultation with health service providers from a broader range of post-secondary institutions, an in-person meeting with research and measurement experts to finalize survey items, and formative testing. The CCWS will play a valuable role in developing population health initiatives targeting the increasing number of young Canadians attending postsecondary institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".