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Record W4402869386 · doi:10.1093/eurpub/ckae114.275

251 Development of the Physical Activity Messaging Guide to Enhance Messaging Practice across HEPA Nations

2024· article· en· W4402869386 on OpenAlexaboutno aff
Chloë Williamson

Bibliographic record

VenueEuropean Journal of Public Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicResearch in Social Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsHEPAText messagingInternet privacyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Abstract Purpose Physical activity (PA) messaging can help change individual and social factors, and thus contribute to improving PA levels, but improving PA messaging practice is warranted. In 2021, the Physical Activity Messaging Framework was published following a robust modified Delphi study. One purpose of this framework is to aid creation of new PA messages. However, a need to enhance dissemination and uptake of this framework in day-to-day practice was identified. Therefore, this project aimed to translate the principles of the framework to develop a practitioner-friendly messaging guide. Methods The framework was adapted and combined with existing evidence to create a step-by-step guide; the Physical Activity Messaging Guide (PAMG). Following this, formal feedback on PAMG was sought through online and in person meetings with members of HEPA/WHO Europe. This feedback was acted on to develop a further draft, which was shared more widely via an online, open-questioned survey (Qualtrics) to gather broader feedback. Results At time of writing, feedback has been gathered from 39 individuals via online survey. Responses were gathered from the UK (including Scotland, Northern Ireland, and England), the United States, Ireland, Germany, Romania, Spain, Portugal, Canada, Australia, and Finland, and came from a mixture of academics, exercise professionals, healthcare professionals, practitioners, and other (e.g., education) professions. Feedback was overall positive, with constructive recommendations for further refinement. Amendments are underway based on feedback to develop a final version of the PAMG, which will be presented at the HEPA 2024 Conference in Dublin, Ireland. Key next steps include translation of the guide into different languages to further enhance uptake. Conclusions Involvement in the Early Career Professional Development Programme has enabled a collaborative approach to develop and refine the PAMG. The PAMG will, as a result, be a more useful tool to various users across countries and contexts. If the PAMG is applied consistently in messaging practice, developed PA messages will be more evidence-based and target-audience focused, ultimately contributing to improving population levels of PA. Funding Source N/A. This work was completed as part of CW’s appointment to the HEPA/WHO Europe Early Career Professional Development Programme 2024.

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)0.057
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0570.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.120
GPT teacher head0.529
Teacher spread0.409 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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