A Nutrition Intervention to Promote the Consumption of Pulse-Based Foods in Childcare Centers: Protocol for a Multimethod Study
Bibliographic record
Abstract
BACKGROUND: Plant-based foods, including pulses (dry beans, lentils, chickpeas, and peas), have gained worldwide attention owing to their health and environmental benefits. Despite high production, the consumption of pulses is low in Canada. Behavior change interventions systematically designed to promote the consumption of pulse-based foods are scarce. OBJECTIVE: We describe the utilization of intervention mapping (IM) in the development of a multicomponent nutrition intervention aimed at promoting consumption of pulse-based foods among preschool children in childcare centers in Saskatchewan, Canada. METHODS: The Pulse Discovery Toolkit intervention was developed following the six steps of the IM protocol. Decisions at each step were either based upon literature review, expert consultation, pretesting, or a combination of these. Following the initial phase of the study, which focused on intervention development, phases II and III of the study were concerned with pilot testing and roll-out of the intervention, respectively. In total, one, two, and four childcare centers participated in phases I, II, and III, respectively. A multimethod approach was designed to evaluate the intervention during pilot testing and roll-out. RESULTS: The application of IM steps 1 to 3 in phase I resulted in the creation of performance objectives at different levels, including at the individual level (preschool children), and the social and environmental levels (parents, early childhood educators, and cooks). These objectives were then used to create a matrix of objectives matching the constructs of the social cognitive theory while taking Piaget cognitive development into consideration. This step was followed by defining program components, implementation, adoption, and evaluation strategies, which were utilized in phases II and III. Data have been collected from 2015 to 2018 and analyzed. The results will be reported elsewhere. CONCLUSIONS: The IM protocol provided a rigorous framework for the development of a multicomponent evidence-based intervention to promote pulse-based foods in childcare centers. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/22775.
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 | 0.047 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.010 |
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".