A mixed methods exploration of the lifestyle drift concept to support the development of measurement tools
Bibliographic record
Abstract
Introduction: Lifestyle drift is a concept that has been used to describe a process whereby policy initiatives designed to support upstream prevention through influencing social determinants drift downstream to a focus on individual behavioural change. This process significantly impacts progress on population-level health equity and there is a critical need to better understand the contributing factors and to learn new ways to mitigate lifestyle drift. This paper describes an initial exploration of the dimensions of lifestyle drift in order to lay a foundation for measurement development. Methods: We conducted a survey to examine the experiences of stakeholders supporting the implementation of prevention and their perceptions of lifestyle drift. The sample consisted of 20 respondents from across Canada working within a range of sectors related to health and youth development. Survey items focused on causes and mechanisms described in the literature. Open-ended questions examined respondent experiences of lifestyle drift issues and their strategies to manage them. Inter-item correlations were used to investigate the relatedness of these initial groupings of the quantitative items and thematic analysis was used to explore the qualitative data. Results: Initial conceptual categories were: funding, individualized behaviours, organizational behaviours, commercial and social determinant of health, medical model and "other." Two major themes emerged from the analysis that focused on factors that contribute to lifestyle drift as well as strategies to mitigate it. Conclusions: This research supports better understanding of contextual issues that influence upstream prevention initiatives. Findings will be useful for future efforts to advance the measurement of lifestyle drift.
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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 | 0.123 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".