Environmental Enrichment for Rats and Mice Housed in Laboratories: A Metareview
Bibliographic record
Abstract
Environmental enrichment has been widely studied in rodents, but there is no consensus on what enrichment should look like or what it should achieve. Inconsistent use of the term “enrichment” creates challenges in drawing conclusions about the quality of an environment, which may slow housing improvements for laboratory animals. Many review articles have addressed environmental enrichment for laboratory rats and mice (Rattus norvegicus and Mus musculus). We conducted a metareview of 29 review articles to assess how enrichment has been defined and what are commonly described as its goals or requirements. Recommendations from each article were summarised to illustrate the conditions generally considered suitable for laboratory rodents. While there is no consensus on alternative terminology, many articles acknowledged that the blanket use of the terms “enriched” and “enrichment” should be avoided. Environmental enrichment was most often conceptualised as a method to increase natural behaviour and improve animal welfare. Authors also commonly outlined perceived risks and requirements of environmental enrichment. We discuss these perceptions, make suggestions for future research, and advocate for the adoption of more specific and value-neutral terminology.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".