Developing and Disseminating Physical Activity Messages Targeting Parents: A Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) messages have demonstrated success in targeting parent support for PA. However, little research exists to inform the development and dissemination of optimally effective PA messages targeting parents. A synthesis of existing literature is necessary to inform message development and dissemination strategies. Unique considerations for parents of children with disabilities (CWD) should be identified given a need for inclusive PA messaging that consider the needs of CWD and their families. METHODS: Systematic scoping methodologies included a peer-reviewed literature search and expert consultation to identify literature regarding PA messages targeting parents, and considerations for parents of CWD. RESULTS: Thirty-four articles that met eligibility criteria were included for examination. Twenty-eight studies were identified regarding the PA messages targeting parents; six themes and 12 subthemes emerged from these articles. Six studies were identified regarding unique considerations for parents of CWD; three themes and four subthemes emerged from these articles. CONCLUSIONS: Through knowledge synthesis, this research can contribute to a knowledge translation process to inform practice guidelines for the development and dissemination of PA messages targeting parents, while also providing unique considerations for PA messages targeting parents of CWD.
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 | 0.057 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".