The <i>Quebec experience</i> in promoting healthy lifestyles and preventing obesity: how can we do better?
Bibliographic record
Abstract
Over the last years, many actions have been implemented in the Canadian province of Quebec to prevent health issues related to diet, physical activity and obesity. As a new public health programme is being launched, the 'How can we do better?' project aimed to identify priority areas for further action. An exhaustive search led to identify 166 interventions rolled out in Quebec between 2006 and 2014. We compared it with evidence-based recommendations. Findings were challenged during a 2-d deliberative forum gathering 25 key stakeholders. At the crossroads of these analyses, 50 proposals emerged to sustain/bolster current efforts or to implement new initiatives. Specific improvements were recommended, e.g. about food supply quality monitoring, healthy food accessibility and affordability, physical activity promotion through land use policies, schools and childcare facilities retrofit and urban planning. Crosscutting proposals stress the importance to implement a new governmental prevention strategy and to reinforce evaluation at all levels. This call for action takes place at a critical period for political commitment and should be maintained until and after curbing the prevalence of obesity and related diseases. Although Quebec-focused, 'How can we do better?' project outcomes may be informative for other jurisdictions, and the methods may be inspiring for those interested in combining knowledge syntheses and deliberative processes to inform decision makers in a limited time frame.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".