Examining public health practitioners’ perceptions and use of behavioural sciences to design health promotion interventions
Bibliographic record
Abstract
BACKGROUND: Behavioural sciences have been shown to support the development of more effective interventions aimed at promoting healthy lifestyles. However, the operationalization of this knowledge seems to be sub-optimal in public health. Effective knowledge transfer strategies are thus needed to optimize the use of knowledge from behavioural sciences in this field. To this end, the present study examined public health practitioners' perceptions and use of theories and frameworks from behavioural sciences to design health promotion interventions. METHODS: This study adopted an exploratory qualitative design. Semi-structured interviews were conducted among 27 public health practitioners from across Canada to explore current intervention development processes, the extent to which they integrate theory and framework from behavioural sciences, and their perceptions regarding the use of this knowledge to inform intervention design. Practitioners from the public sector or non-profit/private organizations who were involved in the development of interventions aimed at promoting physical activity, healthy eating, or other healthy lifestyle habits (e.g., not smoking) were eligible to participate. RESULTS: Public health practitioners generally agreed that behaviour change is an important goal of public health interventions. On the other hand, behavioural science theories and frameworks did not appear to be fully integrated in the design of public health interventions. The main reasons were (1) a perceived lack of fit with current professional roles and tasks; (2) a greater reliance on experiential-produced knowledge rather than academic knowledge (mainly for tailoring interventions to local setting characteristics); (3) the presence of a fragmented knowledge base; (4) the belief that theories and frameworks require too much time and resources to be operationalized; and 4) the belief that using behavioural sciences might undermine partnership building. CONCLUSIONS: This study provided valuable insights that may inform knowledge transfer strategies that could be optimally designed to support the integration of behavioural sciences theories and frameworks into public health practices.
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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 | 0.052 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".