Sensitization Sessions as the Foundation for Training Transformation Activities
Bibliographic record
Abstract
The worldwide rise in obesity makes this the first non-infectious epidemic in human history. The rapid increase is, in fact, influenced more by environment than biology. In an effort to halt the trend, Quebec has launched a major awareness-raising campaign that focuses on healthy environments and targets stakeholders in schools, municipalities, communities and the health sector. The purpose of the present study, then, is to determine how this campaign can promote action towards environments conducive to healthy lifestyles. The theoretical framework is based on planned change. The objectives are to 1) evaluate the quality of awareness-raising methods offered by trainers, and 2) place the impacts of the sessions into perspective. A qualitative approach was prioritized, consisting of two focus groups conducted with 17 trainers. From the standpoint of a healthy environment, sensitization sessions expanded networking, provided a common frame of reference and drove coherent actions for stakeholders involved. As an agent of change, the trainer played a key role in implementing the sessions. The conditions offered encouraged the transition from awareness to information, thereby generating significant results in terms of action. A sensitization session is thus a prerequisite for training transformation activities aimed in innovation.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".