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Record W2280345257 · doi:10.5206/wurjhns.2015-16.4

Nature-Based Physical Activity Advertising: Recommendations Based on Attention Restoration Theory and Psychoevolutionary Theory

2016· article· en· W2280345257 on OpenAlexaffvenue
Adam Gavarkovs

Bibliographic record

VenueWestern Undergraduate Research Journal Health and Natural Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsWestern University
Fundersnot available
KeywordsMoodPromotion (chess)Natural (archaeology)Health benefitsPsychologyHealth promotionPhysical activityAdvertisingApplied psychologySocial psychologyMedicineBusinessPublic healthPolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

Recent research has suggested that physical activity in natural areas may provide benefits that exceed those in other settings. Additional benefits include increased self-esteem and enjoyment of the activity, and decreased negative mood states and blood pressure. Therefore, encouraging nature-based physical activities may play an important role in the promotion of health and wellness. Advertising has been cited as an important component of a health promotion campaign; although to date no study has recommended strategies for designing advertising specific to nature-based physical activities. The purpose of this article is to review two prominent human-nature interaction frameworks, Attention Restoration Theory (ART) and Psychoevolutionary Theory (PET), and based on their tenets, recommend strategies for message design. The two recommendations proposed are: (1) to include natural images that meet the restorative criteria outlined in both theories; and (2) to explicitly feature the additional benefits of exercising in natural spaces in advertisements. Adhering to these recommendations in the advertisement design process may increase the effectiveness of the message.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.406
Teacher spread0.359 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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