MétaCan
Menu
Back to cohort
Record W2163128278 · doi:10.1093/heapro/dau081

Indoor nature exposure (INE): a health-promotion framework

2014· review· en· W2163128278 on OpenAlex
Jill McSweeney, Daniel Rainham, Shannon A. Johnson, Simon Sherry, Jerome F. Singleton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Promotion International · 2014
Typereview
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHealth promotionProcess (computing)PsychologyOrder (exchange)SociologyApplied psychologyComputer sciencePublic healthBusinessMedicineNursing

Abstract

fetched live from OpenAlex

Engaging in outdoor nature-based spaces has significant positive physiological and psychological health benefits. Although the integration of nature into indoor spaces is rarely considered a health-promoting tool, it may be an effective method for increasing nature engagement in a largely urbanized world. This paper presents an overview of indoor nature exposure (INE) by summarizing the current evidence of INE through the use of a scoping methodology. Results show that INE can be a health-promoting tool through the interaction of nature-based stimuli and individual characteristics (e.g. gender, age). Moreover, the results of the current literature need to be interpreted with consideration to methodological issues, such as the lack of participant characteristics, the issue of exposure realism and little qualitative data to highlight individual experiences. The scoping review process allowed for the summation of results and for a framework to be created in order to better understand how INE is facilitated.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.056
GPT teacher head0.395
Teacher spread0.340 · 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