How to engage Children and Families as Part of Multidisciplinary Health Promotion Teams
Bibliographic record
Abstract
Health promotion is the process we employ as health care providers and policy makers to enable people to increase control over, and to improve, their health. Effective health promotion includes several major components, and while policies must be made to promote health, much can be also done by small multidisciplinary teams working with local communities. Importantly such teams are most effective where they include members of the target audience for the health promotion initiative. Consequently where the health of mothers and children is the priority, to engage the target audience effectively, parents and family members including children should contribute as members of the team. Worldwide, education of girls is known to generate multiple health and economic benefits; measures to counter misinformation and use of education programs such as the WHO health promotion model to provide knowledge accompanied by practical health-related skills are of proven value. Effective teams incorporate cultural traits and gender equity into strategies that build resilience and self-regulatory efficacy over social determinants of health. Strategies that help individuals and communities to advance towards the UN sustainable development goals have obvious merit. Health knowledge can be conveyed readily, for example in relation to childhood vaccination, but achieving changes in values, attitudes, and health habits requires effort and innovation by multidisciplinary teams that work synergistically to promote health in an innovative and inclusive manner. The more this is done, the greater the beneficial changes we are likely to achieve.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 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".