Correlates of using behavioural sciences to design health promotion interventions and programs
Bibliographic record
Abstract
Abstract Background The World Health Organization highlighted the need to better integrate behavioural science in the field of public health. Based on the theoretical domains framework, this study examined correlates of public health practitioners’ use of behavioural science principles to plan public health actions aim at promoting physical activity and healthy eating. Methods This study adopted a cross-sectional design. A convenience sample of 160 public health practitioners were recruited from different public health agencies (non-profit organizations, governmental and para-governmental agencies). They were asked to complete a survey questionnaire online and self-reported their use of behavioural science to plan health promotion interventions. Regression analyses were conducted to examine correlates of behaviour and behavioural intentions. All analyses were controlled for sex, number of years of experience in public health and the type of organization they work for. Results Skills (OR = 4.1, 95%CI: 1.3, 13.5) and intentions/goals (OR = 9.2, 95%CI: 2.3, 36.1) were the two domains significantly associated with the use of behavioural science. Perceived capacity to overcome the fact that none of their colleagues rely on behavioural sciences in their organization (OR = 7.2, 95%CI: 1.7, 30.3) was also associated with behaviour. Knowledge (OR = 8.6, 95%CI: 1.9, 39.1) and beliefs about consequences (OR = 4.0, 95%CI: 1.1, 14.7) were in turn associated with intentions/goals. Conclusions This unique study provides important insights for the development of future knowledge transfer activities aim at supporting positive attitudes and motivations toward the use of behavioural science as well as developing competencies and a normative use of behavioural science. The development of a strong knowledge basis would also need to be a core component of trainings to support the integration of behavioural science in public health to design physical active lifestyles and healthy eating interventions. Key messages • Increasing knowledge and skills in behavioural sciences is a key for a better integration in public health practices. • Changes in norms and attitudes toward the use of behavioural sciences will also support a better uptake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".