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Record W4393925423 · doi:10.35898/ghmj-71981

How to engage Children and Families as Part of Multidisciplinary Health Promotion Teams

2024· article· en· W4393925423 on OpenAlexaff
Andrew Macnab

Bibliographic record

VenueGHMJ (Global Health Management Journal) · 2024
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMultidisciplinary approachPromotion (chess)Health promotionPublic relationsPsychologySociologyMedicineNursingPolitical sciencePublic healthSocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.449
Teacher spread0.407 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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