Social Work Research and Global Environmental Change
Bibliographic record
Abstract
Objective: Social workers can help mitigate the human consequences of global environmental change but need an evidence base for appropriate response strategies. This scoping review assesses the state of empirical social work research on global environmental change to identify an agenda for advancing social work research and practice in this area. Method: We searched 5 electronic databases and selected issues/articles for “social work” plus a list of global environmental change topics. Inclusion criteria were: (a) published since January 1, 1985; (b) published in a peer-reviewed journal; (c) empirical; (d) is social work research; and (e) examines at least one topic related to global environmental change. From included studies, we extracted publication year, country setting, global environmental change topic(s), explicit/implicit examination of global environmental change, research design, and study focus. We extracted practice/policy implications as a subgroup. Descriptive statistics and cross tabulations were run in SPSS 23. Results: We identified 112 studies for inclusion. About 1/3 of studies examined hurricanes and typhoons, and most were conducted in U.S., Canadian, or Asian contexts. Many described consequences or coping with change, and although more than 1/3 of studies examined a formal response/intervention, rigorous outcomes-focused research is lacking. Conclusions: Scholars should diversify the topics and global settings that they study, and they should proactively engage with populations and systems before a crisis. There is a need for intervention research on global environmental change—with more rigorous methods of outcome measurement—by social work scholars.
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".