Changes in chronic disease prevention resources and activities in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic disrupted public health efforts for chronic disease prevention (CDP) in Canada and elsewhere. We describe COVID-19-related disruptions in CDP resources and activities among Canadian public health organizations. METHODS: We surveyed all organizations in Canada with mandates for primary CDP, including "resource organizations" that develop or transfer CDP initiatives and "user organizations" that deliver these CDP initiatives to target populations. Key informants most knowledgeable about CDP activities and resources within each organization reported pandemic-related changes in CDP resources and activities. User organizations also reported on the status of 18 specific CDP activities and rated whether pandemic containment measures were barriers to or facilitators of CDP activities. RESULTS: Of the 298 participating organizations (88% response), 129 were resource organizations (37% formally mandated organizations [FMOs]; 63% non-governmental organizations [NGOs]) and 169 were user organizations (48% FMOs; 52% NGOs). Overall, 36% reported decreases in CDP funding (24% major, 12% minor), 30%-41% reported decreases in full-time, volunteer and managerial staff (19%-27% major, 11%-14% minor) and 32% reported decreases in CDP activities (23% major, 9% minor). User FMOs were most affected by decreases. Among user organizations, 16%-39% decreased, suspended or discontinued specific CDP activities. Still, 8%-39% increased their activities, particularly those targeting mental health, marginalized populations, racialized communities and specific gender groups. Half (53%) of user organizations perceived COVID-19 contagion restrictions as barriers to CDP activities. CONCLUSION: Continued monitoring of CDP resources and activities can inform emergency preparedness and ensure that CDP remains a priority during public health crises.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".