Social Work Research and Global Environmental Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.042 | 0.004 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it