Indoor nature exposure (INE): a health-promotion framework
Bibliographic record
Abstract
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.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".