To use or not to use behavioural science evidence in designing health promotion interventions: Identification of targets for capacity building
Bibliographic record
Abstract
The behavioural sciences provide useful evidence to design effective health promotion interventions, but evidence is infrequently integrated in practice. This study examined associations between theoretical domains framework (TDF) constructs and public health practitioners’ use of behavioural science evidence to plan public health actions. Using a cross-sectional design, a convenience sample of 160 practitioners were recruited from public health agencies across Canada. Respondents completed an online questionnaire assessing TDF constructs and the use of behavioural science theory and approaches (i.e., evidence) in their practice. Logistic regression analyses allowed for identification of factors associated with evidence use and intentions. All analyses were adjusted for sex, years of experience, and type of public health agency. Greater skills (OR adj = 4.1, 95%CI 1.3, 13.5) and stronger intentions/aligned goals (OR adj = 9.2, 95%CI 2.3, 36.1) were associated with greater use of behavioural science evidence to plan public health actions. Greater perceived capacity to overcome widespread absence of use of behavioural science evidence in their organization (OR adj = 7.2, 95%CI 1.7, 30.3) was also associated with greater use. More knowledge (OR adj = 8.6, 95%CI 1.9, 39.1) and stronger beliefs about consequences (OR adj = 4.0, 95%CI 1.1, 14.7) were significantly associated with stronger intentions/aligned goals. Findings show that more knowledge, positive attitudes, and stronger perceived competence are associated with greater likelihood of using behavioural science evidence to plan interventions. The use of behavioural science evidence will also require strengthening the norm pertaining to this professional practice in public health organizations.
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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 | 0.074 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".