Promoting Environmental Justice and Sustainability in Social Work Practice in Rural Community: A Systematic Review
Bibliographic record
Abstract
Social work’s response to global climate change has dramatically increased over the last several years. Similarly, growing attention has been paid to rural social work; less clear, however, is how social work, responsive to global climate change, is developed, deployed, and understood in rural contexts; this systematic review elaborates on current social work contributions (research, practice, and policymaking), promoting environmental justice and sustainability in rural communities. Utilizing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach, this article thematically analyzed and synthesized 174 journal articles on social work-specific interventions and environmental justice. The results illustrate insights into the experiences, practices, or objectives of rural social workers vis-à-vis climate change. Significant themes from the literature demonstrated that gender, age, and race limited access to social work services and climate-related disaster response support in rural settings; this article argues that rural community-driven social work practices focused on environmental justice and sustainability should be encouraged and that policy advocacy attentive to climate change and its impact on vulnerable and marginalized groups should be pursued. Current and prospective social work scholars, practitioners, policymakers, and other stakeholders should collaborate with local rural communities to address their unique needs related to climate change. In turn, grassroots strategies should be co-developed to promote climate change adaptation and disaster risk reduction, ultimately achieving the goal of building resilient, healthy, and sustainable rural communities.
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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".