Enhancing the Capacity to Facilitate Physical Activity in Home-Based Child Care Settings
Bibliographic record
Abstract
Healthy Opportunities for Preschoolers (HOP) is a physical activity and movement skill intervention that was developed to address the unique needs of home-based child care providers. The authors used a train-the-trainer approach to enhance local uptake and implementation of HOP and examined the impact on the trainers' (workshop leaders') perceived knowledge, confidence, and intention to implement community workshops and subsequently on the knowledge, confidence, and intentions of workshop participants. This study also assessed feasibility: reach, satisfaction, and facilitators and barriers to workshop implementation. Overall, 92% and 89.5% of the leaders were very or extremely satisfied with the workshop content and delivery, respectively. Training significantly increased their self-reported knowledge (p < .001) and confidence (p < .001). Subsequently, 73% of workshop participants (48 workshops, n = 321) took part in the evaluation; intention to use what they learned after the workshop was high (86%) and perceived knowledge, confidence, and attitude all increased significantly (p < .001). The HOP train-the-trainer approach was feasible and enhanced knowledge, confidence, and readiness to change among home-based child care providers. This approach should be considered as a component of an overall strategy to enhance the promotion of physical activity and movement skills in home-based child care settings.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".