Workplace eco-anxiety: a scoping review of what we know and how to mitigate the consequences
Bibliographic record
Abstract
Purpose Eco-anxiety can affect individuals’ environmental engagement conditions. People spend approximately 35 h or more per week in a workplace environment. It is worth considering whether workplace initiatives exist to deal with eco-anxiety. Little research has been carried out on workplace-related eco-anxiety and no studies have been conducted on how to respond to this health issue within the workplace specifically. To address this issue, we explored eco-anxiety in a workplace context in the literature and developed a model of change to better respond to employees’ eco-anxiety. Methodology First, a scoping review was conducted to investigate workplace eco-anxiety. Second, an analysis was performed in which Lewin’s theory of change was used to propose changes in the workplace designed to better respond to eco-anxiety. Findings Lewin’s three stages can guide action to reduce eco-anxiety in the workplace. Step 1 (unfreeze) involves becoming aware of the eco-anxiety problem within the organization, step two (change) consists of finding alternatives in the built environment such as green spaces and pro-environmental behavior through circular economy practices, and step 3 (refreeze) involves stabilizing this change. Originality Our study is among the few to explore eco-anxiety in the workplace specifically and, to our knowledge the first, to propose an intervention model for management and employees. Lewin’s three stages can guide action to reduce eco-anxiety in the workplace.
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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".