Integration of the Disaster Component into Social Work Curriculum: Teaching Undergraduate Social Work Research Methods Course during COVID-19
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
Abstract This article aims to develop community-contextualised pedagogical innovations to embed disaster components into core social work curriculum through a research methods course. Professional social work education continues to lack a community-contextualised curriculum and professional training that reflects the complexities of extreme events associated with community and human service. This absence jeopardises the advancement of social work engagement in better providing humanitarian support for individuals, families and communities affected by extreme events. Through an undergraduate social work research methods course, this case study qualitatively analysed the instructor’s teaching experience, self-reflection and in-class observation. The study presents three major community-contextualised pedagogical innovations of integrating disaster components into the research methods course: public media critique, amidst-disaster community-based participation and observation and practice situation discussion. These pedagogical efforts support the students’ exploration and development of various research paradigms and strengthen their ability to connect research with practice, thus addressing the community-driven, short-term necessities and long-term development requirements. This contextualising process, which forms a community-based living laboratory, inspires instructors to integrate community-driven characteristics into their pedagogical instruments. The process illustrates a potential pedagogical framework for research methods courses, in particular, and for social work curriculum, in general.
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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 | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.037 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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